Regarding the challenge of handling nested medical entities in Chinese electronic medical records, this study proposes a knowledge-enhanced named entity recognition model for Chinese electronic medical records called ERBEGP based on the RoBERTa-wwm-ext-large pre-trained model. The comprehensive word masking strategy employed by the RoBERTa-wwm-ext-large model can obtain semantic representations at the word level, which is more suitable for Chinese texts. First, the model learns a significant number of medical entity nouns by integrating knowledge graphs, further improving entity recognition accuracy in electronic medical records. Then, the contextual semantic information within the records can be better captured through BiLSTM encoding of the input sequence of medical records. Finally, the efficient GlobalPointer (EGP) model is adopted to simultaneously consider the features of both the head and tail of entities to predict nested entities, addressing the challenge of handling nested entities in named entity recognition tasks of Chinese electronic medical records. The effectiveness of the ERBEGP model is demonstrated by yielding better recognition results on the four datasets within CBLUE.