Text classification of DGCNN model based on deep learning
Di Zhang, Xuefeng Liu · 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) · 2021
This paper focuses on analyzing and studying the application of Recurrent Neural Network (RNN), a deep learning method, in news text classification. Aiming at the problem that the RCNN model cannot fully extract the text context feature information in text classification, and the training complexity of the LSTM method is high, a DGCNN model text classification method based on deep learning is proposed. The DGCNN model adopts dual-channel forward and backward bidirectional threshold recurrent unit (D-GRU) to fully obtain the text context feature information. Tests on the Sogou news corpus show that the improved model has a good classification effect, improving the accuracy, precision, recall and F1value of the classification.