With　the　development　of　deep　learning,　graph　neural　networks　have　attracted　ever-increasing　attention　due　to　their　exciting　results　on　handling　data　from　non-Euclidean　space　in　recent　years.　However,　existing　graph　neural　networks　frameworks　are　designed　based　on　simple　graphs,　which　limits　their　ability　to　handle　data　with　complex　correlations.　Therefore,　in　some　special　cases,　especially　when　the　data　have　interdependence,　the　complexity　of　the　data　poses　a　significant　challenge　to　traditional　graph　neural　networks　algorithm.　To　overcome　this　challenge,　researchers　model　the　complex　relationship　of　data　by　constructing　hypergraph,　and　use　hypergraph　neural　networks　to　learn　the　complex　relationship　within　data,　so　as　to　effectively　obtain　higher-order　feature　representations　of　data.　In　this　paper,　we　first　review　the　basics　of　hypergraph,　then　provide　a　detailed　analysis　and　comparison　of　some　recently　proposed　hypergraph　neural　networks　algorithm,　next　some　applications　of　hypergraph　neural　networks　for　action　recognition　are　listed,　and　finally　propose　potential　future　research　directions　of　hypergraph　neural　networks　to　provide　ideas　for　subsequent　research.