Although　the　classics　of　traditional　Chinese　medicine　are　extensive　and　profound,　the　application　and　research　of　the　deep　learning　model　in　traditional　Chinese　medicine　terminology　recognition　stay　scarce.　In　order　to　make　use　of　the　knowledge　in　these　classics,　a　traditional　Chinese　medicine　terminology　recognition　model　named　BERT-BiLSTM-CRF　is　presented　and　achieve　superior　performance　as　shown　in　the　Results　section.　The　design　process　of　the　BERT-BiLSTM-CRF　model　combines　the　transfer　learning　strategy,　the　pre-training　language　model,　and　the　classical　machine　learning　model.　Specifically,　the　semantic　features　of　TCM　sample　sequences　is　first　extracted　by　transferring　a　BERT　model　that　pre-trained　on　other　large-scale　Chinese　corpora.　Then　go　to　the　BiLSTM　module　to　abstract　the　semantic　features　of　sequence　context.　And　last,　introduce　CRF　to　learn　the　transfer　features　between　context　tags.　In　the　experiment,　the　BERT-BiLSTM-CRF　model　is　compared　with　a　variety　of　benchmark　models　and　outperforms　others.　©　2021　ACM.