In　view　of　the　problem　that　the　global　feature　description　is　overly　dependent　on　the　precise　positioning,　background　subtraction　and　tracking　technology,　and　also　to　address　the　influence　of　the　change　of　the　angle　of　view,　noise　and　occlusion,　action　recognition　methods　in　video　based　on　local　feature　description　were　studied.　A　human　action　recognition　method　based　on　discriminative　regions　was　proposed.　First,　the　video　content　through　iterative　training　and　filter　process　were　analyzed,　and　the　area　of　discrimination　and　distinction　of　regional　representation　in　the　video　was　automatically　extracted.　Then　the　model　statistics　and　the　extracted　discrimination　region　were　described　by　the　bag　of　words.　Finally,　to　determine　the　type　of　human　motion　were　determined　by　the　SVM　(support　vector　machine).　The　methods　proposed　in　this　paper　was　demonstrated　on　the　KTH　and　Youtube　datasets.　Results　show　that　the　method　has　a　high　recognition　accuracy　and　is　especially　insensitive　to　the　complex　background　interference.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.