As　an　emerging　field　of　network　content　production,　live　video　has　been　in　the　vacuum　zone　of　cyberspace　governance　for　a　long　time.　Streamer　action　recognition　is　conducive　to　the　supervision　of　live　video　content.　In　view　of　the　diversity　and　imbalance　of　streamer　actions,　it　is　attractive　to　introduce　few-shot　learning　to　realize　streamer　action　recognition.　Therefore,　a　meta-learning　paradigm　and　CosAttn　for　streamer　action　recognition　method　in　live　video　is　proposed,　including:　(1)　the　training　set　samples　similar　to　the　streamer　action　to　be　recognized　are　pretrained　to　improve　the　backbone　network;　(2)　video-level　features　are　extracted　by　R(2+1)D-18　backbone　and　global　average　pooling　in　the　meta-learning　paradigm;　(3)　the　streamer　action　is　recognized　by　calculating　cosine　similarity　after　sending　the　video-level　features　to　CosAttn　to　generate　a　streamer　action　category　prototype.　Experimental　results　on　several　real-world　action　recognition　datasets　demonstrate　the　effectiveness　of　our　method.