One-class　Support　Vector　Machine　(OC-SVM),　which　is　proposed　to　deal　with　the　problems　of　classification,　intends　to　find　the　smallest　hyper-sphere　containing　the　positive　data.　As　for　the　test　point,　one-class　svm　only　judges　it　whether　the　test　point　belongs　to　that　cluster.　So　OCSVM　is　often　used　in　anomaly　detection.　But　in　the　algorithm　proposed　in　this　paper,　we　first　adopt　shared　nearest　neighbor　algorithm　based　on　the　kernel　method　(KSNN)　to　pre-cluster　the　input　data,　and　then　use　weight　of　each　point,　which　is　produced　by　KSNN,　to　cluster　through　OCSVM.　Experimental　results　show　that　our　algorithm　can　deal　with　some　irregular　distributed　data　and　high-dimension　data　effectively.