For　the　problems　of　irrelevant　frames　and　high　model　complexity　in　action　recognition,　we　propose　a　Spatial-　Temporal　Hypergraph　based　on　Dual　-Stage　Attention　Network　(STHG-DAN)　for　multi-view　data　lightweight　action　recognition.　It　includes　two　stages:　Temporal　Attention　Mechanism　based　on　Trainable　Threshold　(TAMTT)　and　Hypergraph　Convolution　based　on　Dynamic　Spatial-Temporal　Attention　Mechanism　(HG-DSTAM).　In　the　first　stage,　TAM-TT　uses　a　learning　threshold　to　extract　keyframes　from　multi-view　videos,　with　the　multiview　data　serving　as　a　guarantee　for　providing　more　comprehensive　information　subsequently;　In　the　second　stage,　HG-DSTAM　divides　the　human　joints　into　three　parts:　trunk,　hand　and　leg　to　build　spatial-temporal　hypergraphs,　extracts　high　-order　features　from　spatial-temporal　hypergraphs　constructed　of　multi-view　human　body　joints,　inputs　them　into　the　dynamic　spatial-temporal　attention　mechanism,　and　learns　the　intra　frame　correlation　of　multi-view　data　between　the　joint　features　of　body　parts,　which　can　obtain　the　significant　areas　of　action;　We　use　multi-scale　convolution　operation　and　depth　separable　network,　which　can　realize　efficient　action　recognition　with　a　few　trainable　parameters.　We　experiment　on　the　NTU-RGB+D,　NTU-RGB+D　120　and　the　imitating　traffic　police　gesture　dataset.　The　performance　and　accuracy　of　the　model　are　better　than　the　existing　algorithms,　effectively　improving　the　machine　and　human　body　language　interaction　cognitive　ability.