Accurate named entity recognition is the basis of structured electronic medical records and plays an important role in the standardized writing of electronic medical records. However, current word segmentation tools cannot completely and correctly distinguish professional medical terms, making it difficult to achieve structured electronic medical records. As for problems in medical entity recognition, this study proposes an improved deep learning model based on BiLSTM-CRF in the field of named entity recognition. The model combines text and labels as input, which makes the model focus on more useful information in the multi-head attention mechanism. BiLSTM performs feature extraction on the input and obtains the probability of each text on all labels. CRF learns the constraints of the data set during the training and improves the accuracy of the results after decoding. The experiment uses 1 000 manually labeled electronic copies as the data set and the BIO for labeling. Compared with the traditional BiLSTM-CRF model, the proposed model raises the F1 value in the entity category by 3%–11%, verifying its effectiveness in named entity recognition of medical records.