Person　re-　identification　(Re-ID)　has　become　a　hot　research　topic　due　to　its　widespread　applications.　Conducting　person　Re-ID　in　video　sequences　is　a　practical　requirement,　in　which　the　crucial　challenge　is　how　to　pursue　a　robust　video　representation　based　on　spatial　and　temporal　features.　However,　most　of　the　previous　methods　only　consider　how　to　integrate　part-level　features　in　the　spatio-temporal　range,　while　how　to　model　and　generate　the　part-correlations　is　little　exploited.　In　this　paper,　we　propose　a　skeleton-based　dynamic　hypergraph　framework,　namely　Skeletal　Temporal　Dynamic　Hypergraph　Neural　Network　(ST-DHGNN)　for　person　Re-ID,　which　resorts　to　modeling　the　high-order　correlations　among　various　body　parts　based　on　a　time　series　of　skeletal　information.　Specifically,　multi-shape　and　multi-scale　patches　are　heuristically　cropped　from　feature　maps,　constituting　spatial　representations　in　different　frames.　A　joint-centered　hypergraph　and　a　bone-centered　hypergraph　are　constructed　in　parallel　from　multiple　body　parts　(i.e.,　head,　trunk,　and　legs)　with　spatio-temporal　multi-granularity　in　the　entire　video　sequence,　in　which　the　graph　vertices　representing　regional　features　and　hyperedges　denoting　relationships.　Dynamic　hypergraph　propagation　containing　the　re-　planning　module　and　the　hyperedge　elimination　module　is　proposed　to　better　integrate　features　among　vertices.　Feature　aggregation　and　attention　mechanisms　are　also　adopted　to　obtain　a　better　video　representation　for　person　Re-ID.　Experiments　show　that　the　proposed　method　performs　significantly　better　than　the　state-of-the-art　on　three　video-based　person　Re-ID　datasets,　including　iLIDS-VID,　PRID-2011,　and　MARS.