This study proposes a Chinese disease text classification model that integrates knowledge graph. Firstly, by introducing structured knowledge from external medical knowledge graph, a knowledge enhanced disease text vector representation is obtained; Secondly, the global semantic features and local semantic features of the disease text are extracted by using bidirectional long short-term memory network and convolutional neural network respectively. At the same time, the joint attention mechanism improves the efficiency of the model in extracting effective features information; Finally, the extracted features are concatenated and fused, and a classifier is used to output the classification result. The experimental results on the Chinese disease text dataset show that the proposed model has a classification accuracy, recall, and the harmonic mean value F1 of 95.21%, 95.64%, and 95.42%, respectively, which shows better classification performance compared to other models.
