Due　to　the　wide　variety　of　copy　videos,　the　existing　video　copy　detection　methods　using　single　feature　face　great　challenges,　especially　for　video　content　matching,　which　are　difficult　to　deal　with　various　copy　video　transformations.　To　overcome　this　problem,　a　video　copy　detection　method　based　on　sparse　representation　of　MPEG-2　spatial　and　temporal　features　is　proposed　in　this　paper.　Firstly,　the　key　frames　are　extracted　based　on　visual　saliency　model;　Then　the　global　feature　(HSV　color　histograms)　and　local　feature　(ORB　features)　are　extracted　from　the　key　frames;　Meanwhile,　the　key　frames　are　represented　compactly　by　sparse　coding　which　exploits　ORB　features,　and　motion　vectors　(MV)　extracted　from　the　video　bitstreams　are　exploited　to　build　MV　angle　histograms.　Finally,　spatial　feature　and　temporal　feature　are　compared　respectively,　and　matching　results　are　fused　to　generate　the　final　copy　detection　judgement.　The　experimental　results　on　dataset　TRECVID　2009　show　that　the　proposed　method　presents　better　robustness　and　higher　time　efficiency.