To　solve　the　problem　that　a　single　Bidirectional　Long　Short-Term　Memory　(BILSTM)　model　can　only　learn　the　features　of　a　specific　dimension,　and　the　local　dependence　between　characters　is　easily　lost　when　character-level　segmentation　is　used　to　extract　semantic　features,　this　paper　proposes　a　Chinese　medical　named　entity　recognition　method　based　on　multi-segmentation　and　multi-layer　BILSTM.　The　purpose　of　multi-word　segmentation　is　to　enrich　the　semantic　features　that　can　be　used　in　the　model　learning　process,　so　as　to　further　improve　the　entity　recognition　ability　of　the　model.　Multi-layer　bidirectional　long　short-term　memory　can　obtain　feature　information　of　different　dimensions　by　setting　hidden　layers　of　different　sizes,　and　use　the　attention　mechanism　to　capture　global　information.　Experimental　results　show　that　this　method　can　significantly　improve　the　performance　of　Chinese　medical　named　entity　recognition,　and　obtain　78.69%　F　value　in　real　electronic　medical　record　dataset.　©　2022　IEEE.