The Chinese electronic medical record entities contain a large number of medical domain vocabulary and have obvious nested features.When identifying nested entities,there is often a problem of incomplete or inaccurate location of the target entity.To address this problem,a Chinese electronic medical record nested named entity recognition model machine reading comprehension-position information biaffine and MLP(MRC-PBM),based on MRC is proposed.The model transforms named entity recognition(NER)into an MRC task,concatenating the Chinese EMR text and predefined query statements as input,using the medical-based pre-trained model MC_BERT to obtain word vectors,and then using a bidirectional long short-term memory network(BiLSTM)and a multi-granularity expansion convolution model to obtain bidirectional feature information and information between words,respectively,to obtain corresponding feature vectors.Finally,the Hybrid-PBM predictor is used to predict the entities.Experiments are conducted on nested and flat NER datasets.The experimental results show that the proposed model outperforms other mainstream neural network models on the diabetes corpus and public medical datasets,with Fl scores improved by 1.21%to 5.80% compared to baseline models.