Frequent　sequential　pattern　mining　is　an　important　field　in　data　mining.　Compared　with　the　static　data,　the　stream　data　is　a　single　scan　data　obtained　in　a　continuous　and　real-time　way.　The　frequent　pattern　mining　algorithm　of　traditional　static　sequence　database　has　been　difficult　to　meet　the　frequent　pattern　mining　requirements　for　streaming　data.　The　traditional　serial　processing　method　is　time-consuming　and　cannot　meet　the　requirements　of　high　performance　processing.　Based　on　the　existing　Pisa　algorithm,　this　paper　presents　a　parallel　algorithm　named　Parallel-Pisa,　it　can　adjust　the　parallel　strategy　according　to　the　different　velocity　of　the　stream　data　to　improve　the　efficiency　of　the　algorithm　so　that　it　can　be　better　applied　to　frequent　sequence　pattern　mining　of　stream　data.