Research on Chinese news text classification method based on deep neural network

Abstract

In the process of text classification, due to the short length of news headlines and sparse text features, it is difficult for headline samples to provide sufficient and valuable features for classification learning, resulting in low classification accuracy. This paper combines TextCNN with the GRU model, and uses the extraction algorithm proposed in this paper for word embedding, and proposes the TC-GRU (TextCNN-GRU) model. In order to verify the effectiveness of the model in this paper, experiments were carried out on THUCNews, Sogou News Dataset and Fudan University Text Dataset. Experimental results show that the accuracy of the TC-GRU model is generally higher than that of other comparative models.