Early research primarily relied on statistical approaches and hybrids of rule-based and traditional machine learning methods. Naithani proposed an analytical framework that combines natural language processing (NLP) with a support vector machine (SVM) classifier to extract events from messages [16]. However, the model’s performance was limited by substantial noise in the data. Sarker and Gonzalez trained on a combined corpus drawn from three different sources, extracting a rich set of features to train an SVM [17], experiments showed that leveraging features compatible across multiple corpora can significantly improve classification performance. In the PSB social media mining shared task, Chen et al. enhanced model expressiveness and robustness by incorporating multimodal information such as text, images, and knowledge bases [18]. Alizadeh et al. trained SVM and logistic regression classifiers using a rich feature space—including lexicon-based, sentiment, semantic features, and word embeddings—and reported higher accuracy than convolutional neural networks, with sentiment and embedding features contributing most [19]. Bashiri et al. trained an SVM with extensive textual, sentiment, and domain-specific features, demonstrating that the most influential features were general-domain word embeddings, domain-specific embeddings, and domain terminology [20]. To address issues such as feature sparsity and weak semantic signals, Zheng et al. have used topic vectors from the LDA (Latent Dirichlet Allocation) topic–word distribution matrix to reduce dimensionality for short-text classification, while others fused lexical category features with semantics [21]. Although these approaches outperform traditional baselines, they rely on the LDA topic model—an unsupervised method that is relatively slow and sensitive to the choice of the number of topics, thus requiring continual tuning. Another line of work extracts document keywords as text features and achieves competitive results [22]; yet the keyword set grows with the number of documents, inflating the dimensionality of the vector-space matrix and increasing computation. Despite steady gains, these lines of work share constraints that are acute in digital-culture corpora: they depend heavily on hand-crafted or corpus-specific features that are brittle under vernacular drift and platform noise; SVM/logistic pipelines struggle to capture long-range context and compositional semantics, while LDA-based representations are slow, topic-number-sensitive, and require continual retuning; keyword and feature inventories expand with data scale, inflating dimensionality and computation yet still discarding subtle cues via coarse pooling or sparse vectors; and multimodal add-ons often improve coverage but at the cost of complex feature fusion and limited generalization.

In recent years, a wide range of machine learning methods has been applied to text classification. Machine learning, a subfield of artificial intelligence, aims to enable computer systems to process data through automatic learning and performance improvement without explicit programming instructions [23]. Nevertheless, text exhibits weaker regularity, greater arbitrariness, and high complexity, leading to diverse forms and structures; compared with vision, deep learning in the text domain remains relatively less mature [24]. With large-scale data and modern GPU clusters, however, theoretical and computational advances have greatly strengthened the foundation for applying deep learning to text [25].

Within neural architectures, TextCNN, TextRNN, and TextRCNN are widely used models in NLP, each built on distinct neural backbones—convolutional neural networks (CNNs), recurrent neural networks (RNNs), or their combinations—to suit different task requirements [26]. Soni et al. proposed a CNN-based text classifier that uses pre-trained word embeddings as input and applies one-dimensional convolutions to capture word-order information, thereby enriching sentence-level representations [27]. However, CNNs are relatively weak at modeling long-range dependencies, and pooling may discard positional information, risking information loss. Akpatsa et al. first explored joint learning with a hybrid CNN–LSTM architecture for text classification [28], subsequent work by Zhang et al. also fused CNNs and RNNs for short-text classification, leveraging CNNs for local feature extraction and RNNs for long-distance dependencies [29]. To address limitations of pure CNNs and RNNs, Long et al. introduced TextRCNN, which uses a recurrent structure to capture bidirectional context and a max-pooling layer to distill salient features—effectively combining the strengths of RNNs and CNNs while mitigating their respective weaknesses [30]. Despite their progress, these CNN/RNN hybrids still face gaps salient in short, noisy digital-cultural texts: CNNs under-capture long-range semantics; RNNs incur gradient and efficiency costs; and TextRCNN’s max-pooling can discard position- and context-sensitive cues. Many variants also depend on static or task-specific features and grow parameters when stacking or ensembling, which hampers robustness under slang, topic drift, and sparsity.

The introduction of attention mechanisms enables neural networks to focus selectively on salient features, yielding more faithful language modeling. Consequently, attention has been widely adopted across neural architectures. After its first use in NLP by Bhadauria et al. for machine translation, increasing numbers of studies incorporated attention to strengthen feature extraction in diverse models [31]. Sahu et al. proposed a sentence-modeling approach that combines a three-level attention mechanism with CNNs for text classification, achieving strong results [32]. Sherin et al. added bidirectional attention to GRUs for sentiment analysis, enhancing GRU-based classification [33]. Kamyab et al. introduced a bidirectional CNN–RNN architecture fused with attention, where joint learning across deep models improved accuracy [34]. Beyond network hybrids and attention, other improvements have also been explored. Mars et al. introduced BERT, a bidirectional Transformer-based language model trained on large corpora that produces context-dependent word representations [35]. Du et al. integrated logical rules into deep neural networks to mitigate opacity and leverage prior knowledge [36]. Liu et al. proposed ACNN (Attention Convolutional Neural Network), which first uses CNNs for feature extraction and then feeds the features into a self-attention encoder and a context encoder [37]; their outputs are merged and passed to a fully connected layer and a softmax classifier. Chen et al. adapted neural machine translation–style models to text classification by replacing recurrent units in a Seq2Seq framework with Transformer blocks and introducing multi-head attention, thereby better handling long sequences and capturing semantic relations [38]. Wang et al. proposed a model termed B_f that builds on FastText and employs bagging for ensembling [39]. While attention and Transformer-based advances have markedly improved text modeling, many methods remain over-parameterized, compute-intensive, and sensitive to domain drift and short-text sparsity; attention layers can overfit to noisy cues, hierarchical schemes add fusion complexity, and common pooling steps still lose fine-grained, position-aware signals. Moreover, gains from large pretrained encoders can taper off on brevity-dominated, slang-heavy corpora without tailored mechanisms for salient-token selection and feature preservation.

In the broader landscape of text feature augmentation, recent work has moved beyond raw TF-IDF (Term Frequency-Inverse Document Frequency) toward schemes that explicitly enrich or restructure document representations to better align with category semantics. Attieh et al. introduce a category-driven feature engineering (CFE) framework—grounded in a TF-ICF variant—that learns term–category weight matrices and augments documents with synthetic, category-informative features, yielding higher accuracy on five benchmarks with markedly lower compute than deep models [40]. Complementing these supervised augmentations, Rathi et al. survey the mathematical underpinnings and taxonomy of term-weighting, contrasting supervised and statistical families and highlighting how Vector Space Model variants serve as the scaffolding on which many augmentation strategies are built [41]. At the level of weighting functions themselves, Li et al. propose TF-ERF, replacing the logarithmic global component of TF-RF with an exponential relevance frequency to better balance local/global contributions, improving robustness and classification quality on standard corpora [42]. Together, these efforts illustrate a continuum of augmentation—from principled reweighting to category-aware projection and feature synthesis—that improves downstream classifiers while controlling model complexity and training cost.

NLP plays a pivotal role in textual information retrieval. However, most existing classifiers are designed for balanced datasets, making model performance highly sensitive to the size and quality of training data. For Chinese, the relative scarcity and imbalance of corpora substantially degrade precision on positive classes, falling short of practical requirements [43]. Data augmentation is therefore widely used to alleviate these issues by transforming existing samples into novel ones—commonly via synonym replacement or back-translation. Wei et al. introduced EDA (Easy Data Augmentation), which applies four simple edits—random deletion, replacement, swapping, and insertion—to improve text-classification performance and reduce overfitting [44], owing to its randomness, however, EDA can discard information, ignore context, and alter original meanings [45]. With advances in neural machine translation, back-translation has become a popular augmentation technique: text is translated from the source language to an intermediate language and then back to the source to create augmented samples [46]. Xie et al. proposed Unsupervised Data Augmentation (UDA), which augments manually labeled data with distantly supervised signals to construct a larger, more diverse training set and thereby improve generalization and performance across relation-extraction tasks [47]. Zheng et al. further introduced an unsupervised augmentation method that imposes a consistency loss so the model produces stable predictions across differently augmented views [48], experiments on multiple semi-supervised tasks demonstrated significant gains and broad applicability across domains and datasets. Liang et al. proposed a multi-channel neural text classification model that fuses ALBERT embeddings with CNN (local features), GCN and BiLSTM (global spatial–temporal features), then applies softmax, achieving superior accuracy and recall to single-channel and other hybrid baselines on THUCNews [49]. Gao et al. introduced ALBERT-TextCNN-Attention (PATA), which dynamically fuses intermediate ALBERT layers via two channels, adds TextCNN for local sentiment cues and an attention mechanism for richer features, yielding a compact model (≈19.72% of BERT’s parameters) that attains 90.63% accuracy on waimai-10k and outperforms recent ALBERT-based methods [50].

The experimental dataset is a text classification dataset selected from THUCNews (URL: https://github.com/jinmuxige0816/THUCNews/tree/main, accessed on 01 December 2025). It contains a total of 200,000 news headlines, each with a text length between 10 and 30 characters. The dataset covers ten categories: finance, real estate, stock, education, technology, society, current affairs, sports, games, and entertainment. Each category consists of 20,000 text samples, labeled with digits 0–9. In the experiments, 180,000 samples were randomly selected as the training set, 10,000 as the test set, and another 10,000 as the validation set. The experimental environment configurations are shown in Table 2.

In this section, a series of comparative experiments are conducted on three hyperparameters—namely the value of k in the SVD algorithm, the convolution kernel size, and the number of convolution kernels—in order to examine their impact on the classification performance of the proposed model and to determine their optimal settings.

For the SVD algorithm, dimensionality reduction is achieved by retaining the top k singular values and representing the main features of the feature map matrix using the product of the k-order left singular matrix and the singular value matrix, Un×kΣk×k. The choice of k is crucial, as it determines both the reduced feature dimension and the quality of dimensionality reduction. In this study, the maximum dimension of the feature vectors requiring dimensionality reduction via SVD is 90. Therefore, with other parameters fixed, k is varied from 1 to 9. The evaluation indicators used were the time consumption comparison before and after the SVD improvement and the macro-F1 score (macro-F1), in order to analyze the impact of k on the model performance. The experimental results are shown in Fig. 5.

The feature dimension retention parameter k in the SVD algorithm has a significant impact on model performance. When k is too small, insufficient feature dimensions are preserved, leading to information loss and limiting the model’s feature extraction ability. Conversely, when k is too large, redundant dimensions increase model complexity, which may cause overfitting. When k=5, the model achieves the highest macro-F1 score. Therefore, in this study, the SVD algorithm retains five dimensions after feature vector reduction. The runtime curves indicate that computational cost increases almost monotonically with k: for SVD, the time grows from 2.88 to 8.10 h; for MaxPool, from 0.96 to 4.75 h. The performance gap between the two is approximately threefold in the low-k regime, gradually narrowing as k increases but remaining substantial, thereby quantifying the trade-off of performance for time. Balancing performance and efficiency, k=5 yields the optimal macro-level discriminative power with an acceptable computational cost under the current dataset and implementation. This result also supports a practical guideline: when the network and input scale are fixed, selecting a moderate k value that captures the major energy components without excessively increasing the rank allows SVD to effectively exploit its global structural expressiveness while avoiding unnecessary computational overhead and the risk of overfitting.

Model parameters are internal configuration variables, and different parameter settings can significantly affect model performance. Compared with traditional machine learning, deep learning involves larger-scale data and requires a broader range of parameter choices. The hyperparameter settings used in this study are summarized in Table 3.

To ensure efficiency, we first conducted a small-scale hyperparameter search on a fixed validation set and determined the final configuration based on a trade-off between macro-averaged F1 (macro-F1) and training time. Specifically, the learning rate was fine-tuned over 2×10−5,3×10−5,5×10−5,1×10−4 using the AdamW optimizer with linear warm-up followed by a constant schedule. Results indicated that 5×10−5 offered the best convergence speed and stability, and was therefore adopted. The number of training epochs was compared across 2,3,4,5 with early stopping enabled. Performance gains on the validation set saturated after 3 epochs, with signs of slight overfitting, leading to the choice of 3 epochs. The batch size was constrained by GPU memory and effective batch size considerations; a size of 128 provided a good balance between throughput and generalization. The hidden dimension was set to 768, consistent with the backbone size of the pretrained encoder, with no further search. The input sequence length was set to 32 based on a prior analysis of corpus length distribution-this choice maintained a controllable truncation ratio, and increasing it to 64 or 128 yielded marginal macro-F1 improvements at the cost of significantly higher memory and latency. For convolutional layers, kernel sizes (2,3,4) were selected to cover diverse local patterns, showing greater robustness on the validation set compared to (3,4,5). The number of convolutional kernels was set to 256, achieving a good trade-off between representational capacity and parameter efficiency. Dropout was fixed at 0.1, a common setting for fine-tuning pretrained models.

To further illustrate the impact of different architectural enhancements, Fig. 6 compares the performance of baseline models with their variants augmented by attention mechanisms, singular value decomposition (SVD), and the combination of both. The y-axis represents the performance metrics of the evaluated text classification models. Specifically, it shows the Accuracy (%) and F1-score (%), both expressed as percentages.

When evaluating the constructed dataset with classical text classification models, we select four deep learning approaches without BERT or attention—TextCNN, TextRNN, DPCNN, and TextRCNN—and compare performance using accuracy and F1 score. The accuracies of the four models on this dataset are 80.82%, 83.92%, 85.08%, and 81.78%, respectively. Overall, DPCNN performs best in terms of accuracy, reaching 85.08%; its stacked deep convolutional blocks effectively enhance contextual modeling, conferring advantages in capturing long-range dependencies and extracting deep semantic features. In contrast, TextRNN achieves the highest F1 score (86.94%), indicating a better balance of precision and recall for this task. Owing to its recurrent architecture, TextRNN models sequential dependencies particularly well, leading to superior class balance in predictions. Despite the computational efficiency and simplicity of TextCNN’s local convolutional feature extraction, its accuracy (80.82%) and F1 score (81.81%) are lower than those of the other models, reflecting limitations in modeling long texts on this dataset. TextRCNN attains intermediate-to-lower performance, with an accuracy of 81.78% and an F1 score of 81.71%. Although its hybrid convolution–recurrent design captures both local and global features, it does not deliver a marked advantage at larger experimental scales.

After introducing a dual attention mechanism on this dataset, TextRNN_Att stands out, with accuracy rising to 87.78% and F1 to 87.23%, improvements of 3.86 and 0.29 percentage points over the baseline, respectively. TextRCNN_Att also improves noticeably, with accuracy increasing from 81.78% to 84.70% and F1 from 81.71% to 85.80% (gains of 2.92 and 4.09 percentage points), yet its absolute performance still lags behind TextRNN_Att by 3.08 points in accuracy and 1.43 points in F1. For DPCNN, the dual attention mechanism yields a mixed effect—accuracy decreases from 85.08% to 83.17% (−1.91 points) while F1 increases from 82.08% to 83.42% (+1.34 points)—suggesting that dual attention helps balance precision and recall but its global reweighting may partially interfere with DPCNN’s stable extraction of hierarchical n-gram structures. TextCNN_Att attains an accuracy of 80.61% and an F1 of 80.17%, both slightly below the baseline (−0.21 and −1.64 points), indicating that in shallow architectures dominated by local convolutions, dual attention provides limited benefit and may introduce minor performance fluctuations due to increased variance in weight distributions.

Building on the original models, further integrating singular value decomposition (SVD) for feature processing yields substantial gains across all four model types. SVD_TextRCNN exhibits the largest improvement, with accuracy rising from 81.78% to 87.45% (+5.67 points) and achieving the best F1 overall. The dual attention mechanism, through two levels of weighting at the token and sentence levels, both highlights salient information and suppresses irrelevant features, while globally refining the semantic distribution of sentence representations—thereby granting RNN/RCNN-type models stronger advantages in recall and F1. SVD, by performing low-rank matrix factorization for dimensionality reduction and denoising, preserves principal semantic directions and removes high-dimensional redundancy, simultaneously reducing model complexity and improving interclass linear separability; this effect is particularly pronounced for high-dimensional long-sequence representations.

Taken together merely introducing the dual attention mechanism improves F1 for some models but does not fundamentally alter TextRCNN’s relative disadvantage; however, augmenting the representation with SVD markedly enhances separability and generalization for all models, with TextRCNN attaining the highest F1 (88.34%). These results indicate that combining a dual attention mechanism with SVD can further unlock performance potential in the subsequent fusion model and better balance precision and recall in text classification tasks.

To further enhance these SVD-processed models, we introduce an attention mechanism; in particular, adding attention to SVD-TextRCNN yields the method proposed in this paper. After introducing a dual attention mechanism into the four classical text classification models already processed by singular value decomposition (SVD), all models achieve varying degrees of improvement. Among them, SVD_TextRCNN_Att performs the best, reaching an accuracy of 89.76% and an F1 score of 89.49%, which substantiates the significant role of dual attention in strengthening long-sequence dependency modeling and optimizing the fusion of global and local features. SVD_TextRNN_Att attains an accuracy of 89.35% and an F1 of 89.40; compared with SVD_TextRNN (88.82% accuracy, 87.68% F1), this reflects gains of 0.53 percentage points in accuracy and 1.72 points in F1. These results indicate that the dual attention mechanism can further enhance semantic weighting in recurrent architectures that already possess strong sequential modeling capabilities, yielding a more balanced trade-off between precision and recall.

At the feature-processing stage, SVD uses low-rank approximation to compress high-dimensional sparse features, remove noise, and preserve the principal semantic directions, thereby making the input features more compact and discriminative and providing a clearer semantic distribution for downstream classification. Within the model, the dual attention mechanism applies two levels of weighting—word-level and sentence-level—which both highlights the tokens most critical for classification and refines the sentence representation at the global contextual level, organically combining salient local information with global semantic structure. The fusion of these components equips the model with efficient, robust feature representations while enabling dynamic reweighting during inference, markedly improving its ability to capture complex semantic patterns.

This fusion strategy is particularly effective for SVD_TextRCNN: leveraging RCNN’s strong contextual capturing capacity, SVD’s feature compression advantages, and dual attention’s global–local weighting, it attains the highest accuracy and F1, validating the effectiveness and superiority of the proposed method in text classification by balancing precision and recall and enhancing model generalization.

To systematically verify the effectiveness and trainability of the proposed ASSC-TextRCNN for text classification, we conduct controlled experiments using the classical TextRCNN as the baseline. The two models are trained under identical data splits and preprocessing pipelines, and they share the same optimizer, learning-rate schedule, batch size, maximum number of iterations, and regularization strategy. Except for introducing the ASSC module, all other network components and training hyperparameters remain unchanged to ensure fairness. We evaluate classification accuracy (Accuracy) and cross-entropy loss (Loss) on both the training and validation sets, recording them in sync and visualizing them against training steps, so as to examine optimization convergence, generalization behavior, and potential overfitting [57]. We compile statistics for all indicators throughout training before and after the improvement; the comparative trajectories are shown in Fig. 7.

Under the same data and training configuration, the improved ASSC-TextRCNN exhibits clear advantages over the baseline TextRCNN in convergence speed, final performance, and training stability. From the training/validation loss and accuracy curves, ASSC-TextRCNN achieves a rapid loss drop and steep accuracy rise in the early iterations (~1–3k), with training accuracy quickly surpassing 90% and entering a plateau, whereas the baseline requires more iterations to approach a lower plateau—evidence of better optimization attainability and sample efficiency. In terms of final performance, the validation accuracy of ASSC-TextRCNN stabilizes around ~90%, an absolute gain of about 10 percentage points over the ~80% baseline; meanwhile, its validation loss remains around ~0.32–0.35, markedly lower than the baseline’s ~0.55–0.60, indicating reduced generalization error. The training and validation trajectories of ASSC-TextRCNN are overall smoother with smaller oscillations, suggesting a more stable optimization process and lower sensitivity to gradient noise and hyperparameter perturbations. By strengthening contextual representations and inter-class separability, the ASSC mechanism accelerates the formation of discriminative features and smooths decision boundaries, thereby improving both convergence efficiency and generalization. Across accuracy, convergence, and robustness, ASSC-TextRCNN consistently outperforms the baseline, demonstrating clear empirical advantages and practical value.

To comprehensively assess the effectiveness of ASSC-TextRCNN on a 10-class news classification task, we again adopt TextRCNN as the baseline and conduct comparable controlled experiments under identical data splits, preprocessing, and training hyperparameters. Evaluation metrics include per-class Precision, Recall, and F1, which are visualized in three heatmaps for the baseline, the improved model, and their difference, using both color intensity and cell values to encode performance magnitude and improvement.

The improved model achieves consistent and substantial gains on all three macro-averaged metrics in Fig. 8, Precision rises from 0.817 to 0.916, Recall from 0.818 to 0.915, and F1 from 0.817 to 0.915, indicating simultaneous enhancement of discriminability and coverage and a significant reduction in overall error. At the class level, improvements are broad and particularly pronounced for previously weaker categories: for example, stocks see F1 increase from 0.658 to 0.853, with Recall improving from 0.639 to 0.875 and Precision from 0.679 to 0.832; finance improves in F1 from 0.774 to 0.908, with Precision and Recall gains of 0.130 and 0.136, respectively. Even for categories that already performed well, the improved model delivers steady gains—for instance, education F1 rises from 0.903 to 0.953, and sports from 0.938 to 0.977—showing that errors can still be further reduced in high-baseline regimes.

The difference heatmap exhibits a predominantly positive and continuous coloration, reflecting concurrent improvements in Precision and Recall for most categories. These gains do not stem from threshold trade-offs but from systematic enhancements in representational capacity and class separability. Coupled with the larger gains observed in semantically overlapping categories such as stocks, finance, and science, this suggests that the ASSC mechanism more effectively captures long-range context and topical cues, reduces cross-category confusion, and yields smoother, more robust decision boundaries. In summary, under a rigorously fair setting, ASSC-TextRCNN significantly outperforms the baseline at both macro and micro levels, demonstrating broad effectiveness and strong generalization, and providing solid empirical support for scaling to larger and cross-domain datasets.

To examine the discriminative capacity and error structure of ASSC-TextRCNN on the 10-class news task, we conduct strictly comparable controlled experiments with the same data split, preprocessing, and training hyperparameters as TextRCNN, and visualize the test-set confusion matrices of both models side-by-side to characterize inter-class misclassification patterns and their trends (a LogNorm color map is used to enhance readability across different count magnitudes) in Fig. 9.

The diagonal counts for ASSC-TextRCNN increase markedly across all categories, and overall accuracy improves from 81.78% to 91.47%, an absolute gain of +9.69 percentage points. This gain represents a “net improvement” driven by a substantial reduction in systematic confusions rather than a trade-off between categories. For key confusable pairs, bidirectional mistakes between finance and stocks shrink significantly: “finance-stocks” drops from 163 to 75, and “stocks-finance” from 123 to 30, with the recalls of the two classes rising from 0.735 and 0.639 to 0.871 and 0.875, respectively. Confusions due to semantic proximity between science and games also diminish notably—for instance, “games-science” falls from 83 to 30, and “science-stocks/politics/games” decrease from 71/50/58 to 33/20/27 (a total reduction of 99)—raising the science recall from 0.705 to 0.866. For high-baseline categories, the diagonal strengthening in education and sports remains evident, while residual confusions such as “education-entertainment” and “entertainment-sports/games” drop from 34 to 10 and from 27/23 to 12/5, respectively. By integrating context more effectively and shaping decision boundaries, ASSC-TextRCNN systematically reduces cross-class errors caused by topical overlap and lexical co-occurrence, improving recall for weaker classes while further lowering errors for stronger ones. The remaining few symmetric or near-symmetric confusions suggest that future work could incorporate finer-grained domain features and cross-paragraph semantic constraints to eliminate edge cases.

To comprehensively evaluate the overall advantages of the proposed ASSC-TextRCNN, which fuses SVD and attention, on the 10-class news classification task, we conduct comparable controlled experiments under the same data splits, preprocessing pipeline, and training settings as the competing models, and perform a horizontal comparison using accuracy and F1 as metrics across classical sequence models (BiGRU, BiLSTM, FastText), a self-attention model (Transformer), and pretrained language models (BERT, MacBERT, ELECTRA). The results are presented in Table 4. To ensure a fair comparison, all models were trained using an equivalent number of optimization steps and a unified early stopping criterion, rather than a fixed number of epochs. Specifically, we employed the AdamW optimizer with 10\%  warm-up and cosine annealing for learning rate scheduling. Early stopping was monitored based on macro-F1 on the validation set, with a minimum improvement threshold of 0.001 and identical evaluation frequency across models. Additional checkpoints were performed at 3, 5, and 10 epochs. Each experiment was repeated with five different random seeds, and we report the mean and standard deviation of the results. For baselines that did not meet the early stopping criterion within 3 epochs, training was continued until either early stopping was triggered or the shared maximum step count was reached. This setup ensures that all methods are evaluated at their respective optimal performance points.

ASSC-TextRCNN achieves the best performance on both metrics, with accuracy and F1 reaching 91.47% and 91.49%, respectively—an absolute gain of +1.32/+1.37 percentage points over the strongest current comparator, Transformer (90.15%/90.12%), corresponding to an approximate 13.4% reduction in relative error rate. Compared with FastText (89.25%/89.26%) and BERT (89.50%/88.50%), the accuracy gains are +2.22 and +1.97 points, and the F1 gains are +2.23 and +2.99 points, respectively, representing a more pronounced advantage over earlier RNN-based models and related pretrained variants. The concurrent improvements in accuracy and F1 indicate that the gains do not stem from thresholding or incidental class-distribution effects, but from systematic enhancements in representation quality and decision-boundary separability. In latent space, SVD performs low-rank compression that de-redundifies and denoises long texts and topic-mixed scenarios, highlighting sentence-level stable semantic factors; on this foundation, the attention mechanism further focuses on discriminative segments and mitigates confusions caused by cross-class lexical co-occurrence. Consequently, consistent gains are realized atop strong baselines spanning diverse architectures and training paradigms. Together with the evidence from the confusion matrices and per-class metrics, ASSC-TextRCNN substantially boosts recall for weaker classes while further reducing residual errors for stronger classes, demonstrating broad-spectrum efficacy and robust generalization.

Addressing the challenge that digital-culture texts—characterized by being sparse, short, noisy, and highly time-sensitive—are difficult to model effectively with traditional methods, this study proposes and systematically investigates a classification framework based on ASSC-TextRCNN. On top of TextRCNN’s context-aware convolution–recurrent architecture, we introduce ALBERT as a pretrained semantic encoder to enhance deep semantic alignment; we design a short-text–oriented dual-attention mechanism that adaptively focuses on latent key information along the word–context and channel–semantics dimensions; we replace traditional max pooling with singular value decomposition (SVD) to mitigate feature loss from extreme pooling and preserve discriminative subspaces; and we adopt cross-entropy loss to robustly capture class distributions. These architectural refinements strike a better balance among representation depth, information fidelity, and discriminability.

Experiments demonstrate that ASSC-TextRCNN substantially outperforms multiple mainstream deep-learning baselines on digital-culture text classification: relative to the baseline, accuracy improves by 11.85%, F1 by 11.97%, and the relative error rate decreases by 53.18%; the confusion matrix exhibits overall contraction, and class boundaries become clearer. These results indicate that deep coupling of pretrained semantics with contextual structure effectively alleviates the information scarcity of short texts; dual attention can reliably distill key cues under sparsity and noise, enhancing sensitivity to fine-grained class differences; SVD-based subspace compression and reconstruction preserve global discriminative information better than max pooling, improving robustness; and end-to-end optimization with cross-entropy enables stable decision surfaces even under class imbalance and fuzzy boundaries.

Robust understanding of short and nonstandard expressions improves automatic archiving, topic clustering, and metadata annotation for cultural materials. More accurate, fine-grained classification markedly enhances topic routing and audience targeting efficiency, reducing exposure waste from misclassification–misalignment and activating the long tail of high-quality cultural content for precise reach. The “structure-preserving + key-selection” synergy formed by dual attention and SVD effectively suppresses noise interference and semantic drift, supporting public-opinion early warning, content compliance review, and intellectual-property protection for more fine-grained governance. Stable classification signals provide quantifiable evidence for hotspot discovery, issue-evolution tracking, and curation optimization, forming a data-driven, closed-loop content-operations pipeline. ASSC-TextRCNN embodies a synergistic paradigm—from deep semantic pretraining to attention-based selection, structured compression, and robust optimization—validating the effectiveness of structure-preserving dimensionality reduction (SVD) + attention-based discrimination and furnishing core infrastructure for organized representation and at-scale distribution of digital-culture content in complex settings. With stronger semantic capacity and lower misclassification costs, the model advances a shift from experience-driven to data–algorithm co-driven cultural dissemination, accelerating value realization and the sustainable amplification of social impact.

Given the complex ecology of digital-culture texts—sparse, short, noisy, and time-critical—future work on ASSC-TextRCNN should both deepen the technical pathway and broaden cultural applications. On the technical front, we will first tackle the core issue of inadequate information in short texts by exploring a synergy of external knowledge, context modeling, and attention-based selection: atop the current ALBERT backbone, incorporate retrieval-augmented and knowledge-graph–constrained semantic completion, injecting structured relations among cultural entities into encoding and decoding; aggregate multi-granularity context across paragraphs and conversations to mitigate semantic sparsity in individual short texts. Building on dual attention, we will develop interpretable hierarchical attention, enabling causal visualization and controllable guidance of the word–context and channel–semantics foci, thus providing a traceable evidence chain for cultural concepts and narrative themes. In line with the structure-preserving idea behind replacing max pooling with SVD, we will investigate dynamic low-rank decomposition and randomized approximations to reduce latency and memory while maintaining the integrity of the discriminative space, and coordinate with parameter-efficient finetuning to ensure deployability and scalability under platform-level, high-concurrency workloads.

Digital-culture content exhibits substantial diversity in topic, style, and genre, alongside pronounced distributional drift; models must retain stable memory while updating flexibly over time: employ time-aware train/validation splits and imbalance-aware long-tail reweighting to boost recognition of emerging subcultures and niche themes; adapt across languages and dialects to build a multilingual, multi-domain shared semantic subspace, using contrastive learning to align styles across platforms and genres and thereby reduce out-of-domain performance collapse. Weak and distant supervision can rapidly expand label coverage, while active learning—via uncertainty and cost-sensitive sampling—guides human annotation to form an efficient human-in-the-loop pipeline, significantly lowering the cost of introducing new classes and topics.

For interpretability, jointly visualize the contributions of dual attention and the low-rank subspace to clarify which words/subspaces are pivotal to final decisions. Uncertainty estimation and probability calibration will help the system respond cautiously in high-risk or semantically ambiguous scenarios, reducing governance risks from misclassification. Regarding adversarial and noise robustness, systematically evaluate the impact of colloquialisms, orthographic variants, detection-evading metaphors and homophones, and machine-translation noise on decision boundaries, and reinforce with data augmentation and robust optimization to bolster resilience in real-world dissemination environments. For large-scale deployment, integrate fairness and bias auditing end-to-end, with targeted evaluations for minority-language communities, regional dialects, and youth/elderly users, preventing the technological amplification of existing discourse imbalances.