Chinese News Text Classification Method Based On Attention Mechanism

Abstract

Combining the convolution neural network (CNN) model and bidirectional long short-term memory (BiLSTM) model, an ATT-CN-BILSTM Chinese news classification model is proposed based on the attention mechanism. The model uses the attention mechanism to improve the feature extraction process of CNN and BiLSTM. After cancelling the CNN pooling layer, it pays attention to the critical local features obtained by CNN convolution according to the timing features output by BiLSTM, giving full play to the respective advantages of CNN and BiLSTM models. The experimental results on Thucnews dataset show that the accuracy of the model for Chinese news text classification is 97.87%, and the recall rate and F1 score are better than the comparison model.