Research on the Classification of Digital Cultural Texts Based on ASSC-TextRCNN Algorithm

With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the model’s ability to capture and model potential key information in short texts is strengthened. The Singular Value Decomposition (SVD) was used to replace the traditional Max pooling operation, which effectively reduced the feature loss rate and retained more key semantic information. The cross-entropy loss function was used to optimize the prediction results, making the model more robust in class distribution learning. The experimental results indicate that, in the digital cultural text classification task, as compared to the baseline model, the proposed ASSC-TextRCNN method achieves an 11.85% relative improvement in accuracy and an 11.97% relative increase in the F1 score. Meanwhile, the relative error rate decreases by 53.18%. This achievement not only validates the effectiveness and advanced nature of the proposed approach but also offers a novel technical route and methodological underpinnings for the intelligent analysis and dissemination of digital cultural texts. It holds great significance for promoting the in-depth exploration and value realization of digital culture.