The scientific research achievements generated in the field of electricity， such as papers and patents， contain rich information. However， the research on Named Entity Recognition （NER） of electric power text is insufficient. Therefore， a model that can recognize named entities in Chinese electric power text effectively was constructed， and its performance and effectiveness were verified. Firstly， the keywords of electric power literature were crawled for preprocessing and arranging， and a thesaurus of named entities in the electric power field was constructed. Secondly， combining with word segmentation technology， the acquired literature abstracts in the field of electric power were labeled with named entities， and the corpus data of named entity labeling in the field of electric power was generated. In order to improve the representation ability and semantic understanding ability of the model， Transformer encoder mechanism was introduced in BiLSTM-CRF model. In order to improve the adaptability of the model in the electric power vertical field， the knowledge graph between electric power research keywords and words was constructed， and the neighbor matrix of each word fusing the neighbor information was obtained based on this graph. After that， the neighbor information vectors fusing keyword and word knowledge graph entities were obtained. By constructing a dual-branch word embedding vector input layer， it was possible to obtain word embedding vectors containing contextual information and comprehensive keyword neighbor information. Experimental results show that the proposed model has good recognition effect in the electric power domain.