Metro　passenger　flow　prediction　is　a　strategically　necessary　demand　in　an　intelligent　transportation　system　to　alleviate　traffic　pressure,　coordinate　operation　schedules,　and　plan　future　constructions.　Graph-based　neural　networks　have　been　widely　used　in　traffic　flow　prediction　problems.　Graph　Convolutional　Neural　Networks　(GCN)　captures　spatial　features　according　to　established　connections　but　ignores　the　high-order　relationships　between　stations　and　the　travel　patterns　of　passengers.　In　this　paper,　we　utilize　a　novel　representation　to　tackle　this　issue　-　hypergraph.　A　dynamic　spatio-temporal　hypergraph　neural　network　to　forecast　passenger　flow　is　proposed.　In　the　prediction　framework,　the　primary　hypergraph　is　constructed　from　metro　system　topology　and　then　extended　with　advanced　hyperedges　discovered　from　pedestrian　travel　patterns　of　multiple　time　spans.　Furthermore,　hypergraph　convolution　and　spatio-temporal　blocks　are　proposed　to　extract　spatial　and　temporal　features　to　achieve　node-level　prediction.　Experiments　on　historical　datasets　of　Beijing　and　Hangzhou　validate　the　effectiveness　of　the　proposed　method,　and　superior　performance　of　prediction　accuracy　is　achieved　compared　with　the　state-of-the-arts.