Nested named entity recognition (NER) is crucial in processing Chinese electronic medical records (EMRs). Recently, the BERT-based model using CNN and a multi-head Biaffine decoder has shown promising results in nested NER on news datasets. However, this model faces difficulties in dealing with the complex and unevenly distributed entities in Chinese EMRs, resulting in prediction errors. This paper proposes an MC-BERT-CGC model based on MC-BERT semantic features comprising Context-Gated Convolution and multi-head Biaffine decoder. Our model initially incorporates Chinese medical language knowledge by leveraging MC-BERT to represent medical descriptions as sentence vectors. We then use Context-Gated Convolution to accurately define the boundaries of nested entities by learning overlapping relationships between different entities. Finally, we use Focal Loss to classify difficult-to-distinguish entities. Experimental results tested on our Chinese EMRs and the CMeEE-V2 dataset show that our model performs better than existing baseline models in Chinese medical NER tasks. The impacts of this study on the life of patients are significant, as more accurate and detailed medical information can be extracted from EMRs, potentially leading to improved diagnoses, personalized treatment recommendations, and proactive identification of health risks. Our code is available at https://github.com/ymlmorning/MC-BERT-CGC .