User　interests　drift　modeling　is　the　fundamental　and　key　technology　of　Personal　Recommendation　System　(PRS),　and　is　arising　more　and　more　attention　by　researcher　from　home　and　abroad.　At　present,　web　user　interests　drift　model　is　in　its　original　phase　for　the　reason　of　many　aspects,　and　there　also　exist　some　problems　need　to　be　studied　continually　and　deeply.　Considering　of　web　user　interests　might　change　with　time　flying,　huge　of　surfing　data　and　many　influence　factors　need　to　be　concerned,　because　user　interests　changes　constantly　with　time　flying.　A　novel　web　user　interest　drift　pattern　modeling　method　is　proposed.　First,　this　paper　takes　user　interests　as　time　series　via　using　hidden　Markvo　model,　which　may　properly　map　the　sequential　characteristics　of　user　interests.　Second,　improved　GSP　searching　algorithm　is　used　to　find　the　frequent　pattern　from　user　interest　sequence.　Finally,　forgetting　mechanism　is　used　to　solve　interests　drift　problem,　and　time　window　is　used　to　store　current　user　interest　pattern,　and　the　old　interests　will　elapse　with　the　new　interests　coming　constantly.　Plenty　of　online　experiments　were　done　to　verify　the　reasonability　of　the　user　interests　drift　model.　Via　experiments,　conclusion　can　be　drawn　that　this　method　can　work　perfectly　and　efficiently,　which　may　firmly　grasp　user　interests　drift　rule　in　time.　Therefore,　personal　recommendation　system　using　these　interests　drift　model　can　recommend　the　latest　information　for　web　user,　only　according　to　user＇s　potential　interests　changing.