This study proposed a medicine auxiliary diagnosis model based on neural network. The model combines a bidirectional long short-term memory（Bi-LSTM）network and bidirectional encoder representations from transformers (BERT), which can well complete the extraction of local features of Chinese medicine texts. BERT can learn the global information of the text, so use BERT to get the global representation of medical text and then use Bi-LSTM to extract local features. We conducted a large number of comparative experiments on datasets. The results show that the proposed model has significant advantages over the state-of-the-art baseline model. The accuracy of the proposed model is 0.75.

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