Web　farming　can　advance　computational　social　science　into　a　never-end　learning　process,　in　which　social　phenomena　are　dynamically　and　scientifically　understood　based　on　continuously　produced,　updated　and　expired　data　in　the　connected　hyper　world.　Named　entity　recognition　is　a　basic　and　core　task　of　Web　farming.　However,　the　existing　named　entity　recognition　methods　mainly　depend　on　the　complete,　high-quality　and　well-labelled　data　sets　and　cannot　meet　the　requirements　of　real-world　applications.　This　paper　proposes　a　continuous　learning　method　for　recognizing　named　entity　by　introducing　the　Web　farming　mode　of　Web　Intelligence　into　the　recognizing　process.　During　the　on-line　stage,　the　domain　contextual　relevance　of　candidate　entities　is　calculated　by　using　the　domain　discrimination　degree　and　the　domain　dependence　function　for　recognizing　the　target　entities.　During　the　off-line　stage,　an　active　learning　approach　is　designed　to　continuously　improve　the　target　corpus　set　by　binding　density-based　clustering　with　semantic　distance　measurement.　Experimental　results　show　that　the　proposed　method　can　effectively　improve　the　accuracy　of　entity　recognition　and　is　more　suitable　for　real-world　applications.