With　the　development　of　network　and　streaming　media　technology,　network　video　traffic　is　growing　rapidly.　In　order　to　better　control　and　manage　network　traffic　and　guarantee　the　quality　of　service　of　network　video,　it　is　necessary　to　classify　network　video　services　effectively.　In　traffic　identification　and　classification,　feature　analysis　and　acquisition　of　better　features　are　the　key　points　to　achieve　efficient　classification.　Starting　with　the　characteristics　of　packet　size　distribution,　rate,　IP　alternation,　byte　number　ratio　between　downstream　and　upstream,　number　of　sub-stream　fragments　and　average　packet　arrival　time　interval,　this　paper　uses　Support　Vector　Machine　(SVM)　to　verify　the　classification　effect　of　this　feature,　and　achieves　a　high　classification　accuracy