Abstract: Aiming at the problems of low efficiency in the construction of knowledge graph in specific fields, insufficient utilization of existing knowledge graph in the field, and difficulty in extracting domain semantic professional entities from traditional models, a Chinese named entity recognition (NER) model based on Bert (bidirectional encoder representations from transformers) multi knowledge graph fusion and embedding (BERT-FKG) is proposed in this paper. It realizes the attribute sharing among entities through semantic fusion for multiple knowledge graphs and enriches the knowledge of sentence embedding. The proposed model shows better performance in Chinese NER tasks in open domain and medical field. The experimental results show that multiple domain knowledge graphs share the attributes of similar entities by calculating semantic similarity, which can make the model absorb more domain knowledge and improve the accuracy in NER tasks.