Text named entity recognition of Chinese medicine occupies an important position in text mining of traditional Chinese medicine, this article through the BiLSTM - CRF method was carried out on the basis of traditional Chinese medicine text named entity recognition, not only has realized the basic named entity recognition, based on the data set according to the Chinese herbal medicine, the three categories and symptoms, also can used to identify the named entity classes. Sequence annotation was performed on 10292 sentences of TCM related medical cases, and vector construction was conducted based on word2vec to carry out model training iteration. Thus, a TCM named entity recognition model with accuracy rate of 97.23%, recall rate of 89.47% and F value of 88.34% was obtained. Among all kinds of recognition, the accuracy rate of Chinese herbal medicine category identification is 94.41%, recall rate is 94.36% and F value is 94.38%. The precision rate of disease category was 80.92%, recall rate was 80.92%, and F value was 80.92%. The accuracy rate of the symptom category was 75.68%, the recall rate was 81.68%, and the F value was 78.56%. There are many named entity recognition models, but the number of them used for TCM related named entity recognition is very small. Therefore, the establishment of TCM related named entity recognition model will promote the development of TCM text mining more effectively.