Pedestrian　trajectory　prediction　is　a　key　component　for　various　applications　that　involve　human　and　vehicle　interactions,　such　as　autonomous　driving,　traffic　management　and　smart　city　planning.　Existing　methods　based　on　graph　neural　networks　have　limited　ability　to　capture　group　interactions　and　precisely　model　complex　associations　among　multi-agents.　To　solve　these　problems,　we　propose　OST-HGCN,　an　optimized　hypergraph　convolutional　network.　It　models　multi-agent　trajectory　interactions　from　both　temporal　and　spatial　perspectives　using　hypergraph　structures,　and　optimizes　the　spatio-temporal　hypergraph　structure　to　enable　fine-grained　analysis　of　multi-agent　trajectory　motion　intentions　and　high-order　interactions.　We　employ　OST-HGCN　to　a　CVAE-based　prediction　framework,　and　use　the　optimized　hypergraph　structure　to　predict　multi-agent　plausible　trajectories.　We　conduct　extensive　experiments　on　four　real　trajectory　prediction　datasets　of　NBA,　NFL,　SDD　and　ETH-UCY,　and　verify　the　effectiveness　of　the　proposed　OST-HGCN.