In　the　field　of　computer　vision,　crowd　video　analysis　especially　crowd　anomaly　detection　had　received　increasing　attentions　of　research.　Effective　prediction　and　detection　of　the　abnormal　events　in　a　crowded　scene　is　quite　crucial　to　establish　a　safe　and　efficient　public　environment.　In　this　paper,　a　more　effective　algorithm　for　anomaly　detection　is　proposed　based　on　WMHOF(Weighted　Multi-Histogram　of　oriented　Optical　Flow)　in　the　framework　of　sparse　representation　based　algorithm.　On　basis　of　the　MHOF　feature,　an　energy　based　weight　is　introduced　to　increase　its　ability　of　group　behaviors　describing.　Experimental　results　show　that　the　proposed　WMHOF　feature　is　more　sensitive　to　the　movements　in　the　scene,　so　as　to　establish　a　more　effective　normal　behavior　model　for　detecting　of　the　abnormal　ones.　By　defining　of　a　sparse　reconstruction　cost　function,　the　AUC　(the　Area　Under　the　Curve　get　a　1%~2%　improvement　compared　with　other　similar　methods.　©　2017　IEEE.