Traffic　prediction　is　a　critical　function　of　Intelligent　Transportation　Systems.　Inspired　by　Graph/HyperGraph　Neural　Networks　theory,　researchers　have　proposed　a　series　of　effective　methods　for　traffic　prediction　that　have　been　proved　as　significant　successes.　Most　methods　construct　an　unchanging　graph　or　hypergraph　based　on　a　fixed　traffic　network　topology　during　prediction.　These　methods　treat　all　traffic　data　(flow,　speed,　occupancy)　equally,　ignoring　the　different　inherent　attributes　of　traffic　data.　Other　methods　construct　dynamic　graph　or　hypergraph　based　on　traffic　data　but　ignore　the　topological　structure　of　the　road　network　itself.　These　methods　will　decrease　the　accuracy　of　prediction　results.　In　this　paper,　we　propose　an　innovative　framework　for　traffic　data　prediction　based　on　Sub-Hypergraph　and　Knowledge　Distillation　(SHKD),　which　effectively　extracts　traffic　data　features　and　combines　them　with　road　network　topology.　Specifically,　we　first　cluster　traffic　data　based　on　the　inherent　attributes　and　construct　hypergraphs　for　data　with　similar　attributes　to　represent　their　relationships,　referred　to　as　sub-hypergraphs.　Then　a　teacher　network　is　built　from　these　sub-hypergraphs　to　extract　the　data　features　in　traffic,　while　a　student　network　is　constructed　based　on　the　geographical　connectivity　among　roads　to　extract　global　topological　features.　To　integrate　the　two　types　of　features,　we　apply　a　knowledge　distillation　method　to　transfer　the　data　features　learned　by　the　teacher　network　into　the　training　process　of　the　student　network,　yielding　the　final　prediction　results.　The　proposed　method　has　been　assessed　with　several　real-world　datasets　in　predicting　traffic　status.　The　experimental　results　demonstrate　the　effectiveness　of　the　proposed　method.