To　address　the　challenges,which　are　the　limited　number　of　domain　entitiesandtherelative　lack　ofcorpus　samples,for　entity　recognition　in　the　fine-grained　domain,　an　unsupervised　method　for　narrow-domain　entity　recognition　was　proposed　by　integrating　word　frequency　and　context　information.Firstly,　fusing　the　word　frequency　and　context　information,　the　new　relevance　hypothesis　with　term-corpus　was　designed,　and　the　probability　of　hypothesis　was　calculated　by　using　log　likelihood　ratio　to　obtain　domain　discrimination　degree　of　candidate　entities.　Based　on　the　relative　domain　ratio　of　head-word　of　candidate　entities　in　the　corpus,　the　domain　dependence　function　was　constructed　to　recognize　the　domain　tendency　of　the　candidate　entities;　Finally,　combining　the　domain　discrimination　degree　and　the　domain　dependence,　the　domain　relevance　measurement　of　the　candidate　entities　was　calculated,　and　the　candidate　entities　whose　domain　relevance　measurement　were　greater　than　the　threshold　were　selected　as　the　narrow　domain　entities.　The　experimental　results　show　that　the　proposed　method　can　improve　the　accuracy　of　narrow-domainentity　recognition　and　reduce　manual　intervention　in　the　recognition　process.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.