In　the　process　of　medicine　information　extraction,　there　are　many　Named　Entity　(NE)　need　to　be　recognized.　But　currently　the　research　on　identification　of　NE　in　the　field　of　medicine,　such　as　physician,　hospital,　disease,　medicine　NE　etc.　is　rarely.　So　in　this　paper　we　present　an　new　approach　for　Named　Entity　Recognition　(NER)　in　the　field　of　medicine　based　on　Bootstrapping　method.　This　method　primarily　consists　of　two　key　steps.　The　first　step　is　Bootstrapping　training　procedure.　Introduction　of　Bootstrapping,　a　self-expanding　technology,　make　this　method　get　rid　of　human　supervision.　The　idea　is　to　start　from　a　initial　feature　set　extracted　from　contextual　information　of　a　little　annotated　corpus　that　corresponding　to　the　concept　for　the　target　NE　type,　and　successively　form　a　complete　feature　set　of　NE.　The　other　is　NER　procedure.　The　feature　set　of　NE　adopted　from　Bootstrapping　training　procedure　is　expressed　as　class　feature　vector　(CFV).　Contextual　information　of　candidate　NE　is　expressed　as　example　feature　vector　(EFV).　The　degree　of　similarity　of　CFV　and　EFV　determines　whether　the　candidate　is　a　NE.　We　carried　out　several　experiments　using　corpus　from　the　internet　for　physician　and　disease　NER.　The　F-measure　of　physician　and　disease　NER　on　the　test　data　is　0.926　and　0.969　respectively.　The　result　above　show　that　the　Bootstrapping　method　has　the　key　advantage　of　NER.