Recently,　lexicon-based　Chinese　Named　Entity　Recognition　(NER)　models　have　achieved　state-of-the-art　performance　by　benefiting　from　the　rich　boundary　and　semantic　information　contained　in　the　lexicon.　However,　in　the　Chinese　medical　domain,　it＇s　difficult　to　obtain　the　medical　lexicon　related　to　the　target　medical　corpus.　In　this　paper,　we　propose　a　new　paradigm,　enhancing　Chinese　medical　NER　with　Auto-mined　Lexicon　(ALNER),　which　alleviates　the　difficulty　of　obtaining　the　medical　lexicon　by　designing　a　data-driven　automatic　lexicon　construction　method.　We　define　medical　lexicon　construction　as　a　high-quality　phrase　mining　task.　We　perform　secondary　annotation　on　the　NER　annotated　data　and　use　the　secondary　annotated　data　to　train　a　deep　learning-based　phrase　tagger.　Experimental　results　show　that　our　method　can　be　combined　with　different　lexicon-based　Chinese　NER　models　to　improve　performance　and　that　the　method　does　not　require　an　external　medical　lexicon.　©　2022　IEEE.