Recently,　action　recognition　has　attracted　considerable　attention　in　the　field　of　computer　vision.　In　dynamic　circumstances　and　complicated　backgrounds,　there　are　some　problems,　such　as　object　occlusion,　insufficient　light,　and　weak　correlation　of　human　body　joints,　resulting　in　skeleton-based　human　action　recognition　accuracy　being　very　low.　To　address　this　issue,　we　propose　a　Multi-View　Time-Series　Hypergraph　Neural　Network　(MV-TSHGNN)　method.　The　framework　is　composed　of　two　main　parts:　the　construction　of　a　multi-view　time-series　hypergraph　structure　and　the　learning　process　of　multi-view　time-series　hypergraph　convolutions.　Specifically,　given　the　multi-view　video　sequence　frames,　we　first　extract　the　joint　features　of　actions　from　different　views.　Then,　limb　components　and　adjacent　joints　spatial　hypergraphs　based　on　the　joints　of　different　views　at　the　same　time　are　constructed　respectively,　temporal　hypergraphs　are　constructed　joints　of　the　same　view　at　continuous　times,　which　are　established　high-order　semantic　relationships　and　cooperatively　generate　complementary　action　features.　After　that,　we　design　a　multi-view　time-series　hypergraph　neural　network　to　efficiently　learn　the　features　of　spatial　and　temporal　hypergraphs,　and　effectively　improve　the　accuracy　of　skeleton-based　action　recognition.　To　evaluate　the　effectiveness　and　efficiency　of　MV-TSHGNN,　we　conduct　experiments　on　NTU　RGB+D,　NTU　RGB+D　120　and　imitating　traffic　police　gestures　datasets.　The　experimental　results　indicate　that　our　proposed　method　model　achieves　the　new　state-of-the-art　performance.