Research on Enhanced Chinese Text Classification Through Feature Confusion and Hierarchical Perception

Conventional text classification models experience inadequate contextual information extraction, restricted classification Accuracy, and significant text information loss. This article presents an improved Chinese text classification model that incorporates feature obfuscation and hierarchical perception. A text sequence channel is established, employing the RoFormer model to increase the positional attributes of word embeddings. Subsequently, multi-scale window convolutions acquire multidimensional feature information within the sequence. An enhanced confusion technique ultimately improves the model’s resilience to interference and finalizes feature confusion fusion. Evaluation on the Waimai, ChnSentiCorp, and COLD datasets indicates that this model attains enhanced accuracy and F1 scores relative to previous benchmark models, confirming its efficacy and relevance.