In view of the poor performance exhibited by traditional words or word vectors in expressing context semantics, as well as the insufficiency of traditional RNN parallel computing ability in Chinese medical EMR named entity recognition, a named entity recognition model of medical EMR based on Bert has thus been proposed. In this model, the BERT pre-training language model can better represent the context semantics in electronic medical records, with the iterative expanded convolutional neural network (IDCNN) characterized with a better recognition effect on convolutional coding of local entities, and with the multiple head attention (MHA) computing the attention probability of each word and all words for many times to obtain the long-distance dependence of EMR sentences. The experimental results show that the BERT-IDCNN-MHA-CRF model can better identify medical entities in electronic medical records, and compared with the baseline model, the precision, recall and F1 values of the model are increased by 1.80% , 0.41% and 1.11% respectively.