Named　Entity　Recognition　(NER)　is　a　basic　task　of　natural　language　processing　and　an　indispensable　part　of　machine　translation,　knowledge　mapping　and　other　fields.　In　this　paper,　a　fusion　model　of　Chinese　named　entity　recognition　using　BERT,　Bidirectional　LSTM　(BiLSTM)　and　Conditional　Random　Field　(CRF)　is　proposed.　In　this　model,　Chinese　BERT　generates　word　vectors　as　a　word　embedding　model.　Word　vectors　through　BiLSTM　can　learn　the　word　label　distribution.　Finally,　the　model　uses　Conditional　Random　Fields　to　make　syntactic　restrictions　at　the　sentence　level　to　get　annotation　sequences.　In　addition,　we　can　use　Whole　Word　Masking　(wwm)　instead　of　the　original　random　mask　in　BERT＇s　pre-training,　which　can　effectively　solve　the　problem　that　the　word　in　Chinese　NER　is　partly　masked,　so　as　to　improve　the　performance　of　NER　model.　In　this　paper,　BERT-wwm　(BERT-wwm　is　the　BERT　that　uses　Whole-Word-Masking　in　pre　training　tasks),　BERT,　ELMo　and　Word2Vec　are　respectively　used　for　comparative　experiments　to　reflect　the　effect　of　bert-wwm　in　this　fusion　model.　The　results　show　that　using　Chinese　BERT-wwm　as　the　language　representation　model　of　NER　model　has　better　recognition　ability.　©　2020　ACM.