Live　video　hosted　by　streamer　is　being　sought　after　by　more　and　more　Internet　users.　A　few　streamers　show　inappropriate　action　in　normal　live　video　content　for　profit　and　popularity,　who　bring　great　harm　to　the　network　environment.　In　order　to　effectively　regulate　the　streamer　behavior　in　live　video,　a　strea-mer　action　recognition　method　in　live　video　with　spatial-temporal　attention　and　deep　dictionary　learning　is　proposed　in　this　paper.　First,　deep　features　with　spatial　context　are　extracted　by　a　spatial　attention　net-work　to　focus　on　action　region　of　streamer　after　sampling　video　frames　from　live　video.　Then,　deep　fea-tures　of　video　are　fused　by　assigning　weights　with　a　temporal　attention　network　to　learn　the　frame　attention　from　an　action.　Finally,　deep　dictionary　learning　is　used　to　sparsely　represent　the　deep　features　to　further　recognize　streamer　actions.　Four　experiments　are　conducted　on　a　real-world　dataset,　and　the　competitive　results　demonstrate　that　our　method　can　improve　the　accuracy　and　speed　of　streamer　action　recognition　in　live　video.　(c)　2021　Elsevier　B.V.　All　rights　reserved.