Hypergraphs　have　received　widespread　attention　in　modeling　complex　data　correlations　due　to　their　superior　performance.　In　recent　years,　some　researchers　have　used　hypergraph　structures　to　characterize　complex　non-pairwise　joints　in　the　human　skeleton　and　model　higher-order　correlations　of　the　human　skeleton.　However,　traditional　methods　of　constructing　hypergraphs　based　on　physical　connections　ignore　the　dependencies　among　non-physically　connected　joints　or　bones,　and　it　is　difficult　to　model　the　correlation　among　joints　or　bones　that　are　highly　correlated　in　human　action　but　are　physically　connected　at　long　distances.　To　address　these　issues,　we　propose　a　skeleton-based　action　recognition　method　for　hypergraph　learning　based　on　skeleton　correlation,　which　explores　the　effects　of　physically　and　non-physically　connected　skeleton　information　on　accurate　action　recognition.　Specifically,　in　this　paper,　spatio-temporal　correlation　modeling　is　performed　on　the　natural　connections　inherent　in　humans　(physical　connections)　and　the　joints　or　bones　that　are　more　dependent　but　not　directly　connected　(non-physical　connection)　during　human　actions.　In　order　to　better　learn　the　hypergraph　structure,　we　construct　a　spatio-temporal　hypergraph　neural　network　to　extract　the　higher-order　correlations　of　the　human　skeleton.　In　addition,　we　use　an　attentional　mechanism　to　compute　the　attentional　weights　among　different　hypergraph　features,　and　adaptively　fuse　the　rich　feature　information　in　different　hypergraphs.　Extensive　experiments　are　conducted　on　two　datasets,　NTU-RGB+D　60　and　Kinetics-Skeleton,　and　the　results　show　that　compared　with　the　state-of-the-art　skeleton-based　methods,　our　proposed　method　can　achieve　an　optimal　level　of　performance　with　significant　advantages,　providing　a　more　accurate　environmental　perception　and　action　analysis　for　the　development　of　embodied　intelligence.