Research on Chinese Text Classification Based on Improved RNN

The traditional RNN model uses the hidden state of the time step for information storage in dealing with the Chinese text classification problem, which causes the subsequent vocabulary to have a greater impact on the classification results, and the classification accuracy is thus affected. To improve the model structure, two improvement methods are proposed in this paper. First, the word vector embedding is performed and then the BiLSTM model is connected to extract the global text features in both directions. secondly, the Attention calculation module is added to the traditional structure for deep feature interaction, or the pooling module of CNN is connected to perform feature dimension reduction. The classification efficiency and accuracy of the model are improved. In the experiment, the classification accuracy rate on the Chinese news dataset reached a maximum of 91.08%, which was 1.73% higher than the highest value of the benchmark model, which effectively improved the performance of the model for Chinese text classification.