The　wide　adoption　of　electronic　medical　record　(EMR)　systems　causes　rapid　growth　of　medical　and　clinical　data.　It　makes　the　medical　named　entity　recognition　(NER)　technologies　become　critical　to　find　useful　patient　information　in　the　medical　dataset.　However,　the　medical　terminologies　usually　have　the　characteristics　of　inherent　complexity　and　ambiguity,　it　is　difficult　to　capture　context-dependency　representations　by　supervision　signal　from　a　simple　single　layer　structure　model.　In　order　to　address　this　problem,　this　paper　proposes　a　hybrid　model　based　on　stacked　Bidirectional　Long　Short-Term　Memory　(BILSTM)　for　medical　named　entity　recognition,　which　we　call　BSBC　(BERT　combined　with　stacked　BILSTM　and　CRF).　First,　we　use　Bidirectional　Encoder　Representation　from　Transformers　(BERT)　to　perform　unsupervised　learning　on　an　unlabeled　dataset　to　obtain　character-level　embeddings.　Then,　stacked　BILSTM　is　utilized　to　obtain　context-dependency　representations　through　the　multi　hidden　layers　structure.　Finally,　Conditional　Random　Field　(CRF)　is　used　to　predict　sequence　tags.　The　experiment　results　show　that　our　method　significantly　outperforms　the　baseline　methods,　it　serves　as　a　strong　alternative　approach　compared　with　traditional　methods.