Named　entity　recognition　is　a　basic　and　core　task　of　biomedical　text　mining.　Comparing　with　other　named　entity　recognition　methods,　methods　based　on　domain　relevance　measurement　need　the　smaller　training　corpora　and　entity　samples　and　are　appropriate　for　recognizing　narrow-domain　entities,　which　belong　to　a　subdivision　and　small　semantic　class.　However,　how　to　obtain　the　high-quality　target　corpus　set　become　a　key　issue.　This　paper　propose　a　biomedicine　named　entity　recognition　method　by　integrating　domain　contextual　relevance　measurement　and　active　learning.　Firstly,　binding　with　density-based　clustering　and　semantic　distance　measurement,　the　representative　and　informative　contexts　are　selected　to　construct　the　target　corpus　set　by　an　active　learning　approach.　Secondly,　the　domain　contextual　relevance　of　candidate　entities　is　calculated　by　using　Domain　the　discrimination　degree　and　domain　dependence　function　for　recognizing　the　target　entities.　Experimental　results　show　that　the　proposed　method　can　effectively　reduce　training　time　and　improve　the　accuracy　of　entity　recognition.