Convergence of polarized self-attention with consistent rank Chinese text classification

Utilizing the powerful feature extraction capabilities of deep learning, a text classification algorithm with multi-dimensional and high-domain adaptability is designed in this study. This method enhances the models understanding of topics and content by incorporating the Polarized Self-Attention (PSA) module, which strengthens the spatial structure and semantic features of textual information. The loss function is redesigned to assign smaller losses to misclassifications of neighboring categories, allowing the model to optimize classification accuracy while learning hierarchical structural information between categories. Finally, experimental verification is conducted on a publicly available news dataset, demonstrating improved results in text classification achieved by the proposed algorithm.