Purpose/Significance To study the method of extracting medical terms from Chinese medical records, to realize the automatic structure of medical records, and to provide structured data for knowledge discovery of medical records. Method/Process The paper proposes a deep learning named entity recognition (NER) model based on BERT combining long short-term memory (LSTM), conditional random fields (CRF) and radical features. This model embeds Chinese radicals in BERT word vector, extracts entity features with BiLSTM, and uses CRF for sequence prediction. 400 medical cases with a total of more than 50 000 words manually marked are divided into training set and test set according to 3∶1, the model is used to identify four types of named entities in Chinese medical records, namely body, medicine, symptom, and disease. Result/Conclusion The F1 value of this model on the test set is 84.81%, which is superior to other models without embedded radicals, indicating that the model can more effectively identify named entities in Chinese medical records and better structured medical records.