Multi-feature Fusion Based on Semantic Understanding Attention Neural Network for Chinese Text Categorization

In Chinese text categorization tasks, the locations of the important features in the Chinese texts are disperse and sparse, and the different characteristics of Chinese texts contributes differently for the recognition of their categories. In order to solve the above problems, this paper proposes a multi-feature fusion model Three Convolutional neural network paths and Long short term memory path fused with Attention neural network path (3CLA) for Chinese text categorization, which is based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and semantic understanding attention neural networks. The model first uses text preprocessing to finish the segmentation and vectorization of the Chinese text. Then, through the embedding layer, the input data are sent to the CNN path, the LSTM path and the attention path respectively to extract text features of different levels and different characteristics. Finally, the text features are fused by the fusion layer and classified by the classifier. Based on the Chinese corpus, the text classification experiment is carried out. The results of the experiments show that compared with the CNN structure model and the LSTM structure model, the proposed algorithm model improves the recognition ability of Chinese text categories by up to about 8%.