Accurate　prediction　of　origin-destination　(OD)　demand　is　critical　for　service　providers　to　efficiently　allocate　limited　resources　in　regions　with　high　travel　demands.　However,　OD　distributions　pose　significant　challenges,　characterized　by　high　sparsity,　complex　spatial　correlations　within　regions　or　chains,　and　potential　repetition　due　to　the　recurrence　of　similar　semantic　contexts.　These　challenges　impede　traditional　graph-based　approaches,　which　connect　two　vertices　through　an　edge,　from　performing　effectively　in　OD　prediction.　Thus,　we　present　a　novel　multichannel　hypergraph　convolutional　neural　network　(MC-HGCN)　to　overcome　the　above　challenges.　The　model　innovatively　extracts　distinctive　features　from　the　channels　of　inflows,　outflows,　and　OD　flows,　to　conquer　the　high　sparsity　in　OD　matrices.　High-order　spatial　proximity　within　regions　and　OD　chains　are　then　modeled　by　the　three　adjacency　hypergraphs　constructed　for　the　above　three　channels.　In　each　adjacency　hypergraph,　multiple　neighboring　stations　are　treated　as　vertices,　while　multiple　OD　pairs　constitute　hyperedges.　These　structures　are　learned　by　hypergraph　convolutional　networks　for　latent　spatial　correlations.　On　this　basis,　a　semantic　hypergraph　is　created　for　the　OD　channel　to　model　OD　distributions　lacking　spatial　proximity　but　sharing　semantic　correlations.　It　utilizes　hyperedges　to　represent　semantic　correlations　among　OD　pairs　whose　origins　and　destinations　both　possess　similar　point-of-interest　(POI)　functions,　before　learned　by　a　hypergraph　convolutional　network　(HGCN).　Both　spatial　and　semantic　correlations　intrinsic　to　OD　flows　are　accordingly　captured　and　embedded　into　a　gated　recurrent　unit　(GRU)　to　unveil　hidden　spatiotemporal　dependencies　among　OD　distributions.　These　embedded　correlations　are　ultimately　integrated　through　a　multichannel　fusion　module　to　enhance　the　prediction　of　OD　flows,　even　for　minor　ones.　Our　model　is　validated　through　experiments　on　three　public　datasets,　demonstrating　its　robust　performances　across　long　and　short　time　steps.　Findings　may　contribute　theoretical　insights　for　practical　applications,　such　as　coordinating　traffic　scheduling　or　route　planning.