Check-in　data　obtained　from　Location-based　Social　Network　(LBSN)　is　a　sort　of　crowd　geographic　data　which　will　reveal　daily　activities　of　urban　residents.　Different　check-in　behaviors　with　the　same　check-in　location　will　produce　the　phenomenon　of　location　duplication　because　of　location　candidate　function　in　LBSN　system.　The　current　density-based　spatial　clustering　algorithms　have　the　following　problems:　difficulty　to　find　density　peak　point.　clustering　error　caused　by　check-in　point　objects　with　duplicate　positions.　In　order　to　solve　these　problems,　we　proposed　a　fast　search　density　peaks　and　clustering　method　for　check-in　data,　based　on　clustering　by　fast　search　and　find　of　density　peaks　(CFSFDP).　Firstly,　position　repetition　frequency　was　introduced　and　calculated　to　illustrate　the　number　of　the　check-in　position　duplications　data.　Secondly,　a　new　type　of　point　feature　was　constructed　by　adding　position　repetition　frequency　of　the　original　check-in　position　data,　which　was　used　as　study　object　to　search　density　peaks.　At　last,　clustering　algorithm　based　on　density　peak　point　was　constructed　in　which　density　connectivity　was　taken　into　account　to　ensure　the　continuity　and　integrity　of　density　clusters.　Taking　check-in　data　obtained　from　Sina　Microblog　as　an　example,　an　experiment　was　designed　and　implemented.　The　results　demonstrates:　Clustering　method　can　effectively　avoid　the　problem　that　the　outlier　location　object　with　high　repeatability　is　chosen　as　the　peak　and　clustering,　and　has　excellent　spatial　adaptability　as　well　when　comparing　with　check-in　data　from　other　area.　Extracted　density　peak　points　can　not　only　be　used　to　represent　the　center　of　the　hot　zone,　but　also　reflect　the　concentration　trend　of　the　hot　zone,　which　can　help　to　explore　the　dynamic　change　of　the　hot　zone.　©　2017,　Surveying　and　Mapping　Press.　All　right　reserved.