Research on TextCNN Model Optimization and Performance Enhancement for Text Classification
A solution based on attention mechanism was proposed to address the issue of insuffi-cient capturing of key information by the TextCNN model,which affects the classification ac-curacy.Multiple-scale convolutional kernels were employed to capture features of different granularities in the text,thereby enhancing the classification accuracy.Subsequently,dropout was applied to the attention-weighted output.Through this approach,dropout could reduce the ex-cessive influence of specific parts on the final output while retaining attention information,thus improving the models generalization ability.Experimental results demonstrate that the improved TextCNN model achieved an accuracy of 92.27％,precision of 92.32％,recall of 92.27％,and Fl score of 92.28％in text classification,surpassing the original TextCNN,TextRNN and Tex-tRNN-attention models.Moreover,performance metrics for each category were also enhanced.
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