Spatio-temporal　prediction　is　a　pivotal　service　for　smart　city　applications,　such　as　traffic　and　air　quality　prediction.　Deep　learning　models　are　widely　employed　for　this　task,　but　the　effectiveness　of　existing　methods　heavily　depends　on　large　amounts　of　data　from　urban　sensors.　However,　in　the　early　stages　of　smart　city　development,　data　scarcity　poses　a　significant　challenge　due　to　the　limited　data　collected　from　newly　deployed　sensors.　Moreover,　transferring　data　from　other　resource-rich　cities　is　typically　infeasible　because　of　strict　privacy　policies.　To　address　these　challenges,　we　propose　a　relational　fusion-based　hypergraph　neural　network　(RFHGN)　for　few-sample　spatio-temporal　prediction.　RFHGN　is　trained　directly　on　limited　data　within　a　city,　exploiting　multiple　spatial　correlations　and　hierarchical　temporal　dependencies　to　enrich　spatio-temporal　representations.　Specifically,　to　enhance　spatial　expressiveness,　we　design　a　high-order　spatial　relation-aware　learning　module　with　an　adaptive　time-varying　hypergraph　structure.　This　structure　is　learned　by　integrating　observational　data　and　is　iteratively　updated　during　training,　enabling　the　capture　of　dynamic　high-order　interactions.　By　combining　these　interactions　with　pairwise　spatial　representations,　we　derive　mixed-order　spatial　representations.　To　reduce　potential　redundancy,　we　introduce　a　regularized　independence　loss　to　ensure　the　independence　of　pairwise　and　high-order　spatial　representations.　Additionally,　to　effectively　capture　temporal　dependencies　at　micro　and　macro　levels,　we　develop　a　hierarchical　temporal　relation-aware　learning　module.　Extensive　experiments　on　three　spatio-temporal　prediction　tasks:　traffic　flow,　traffic　speed,　and　air　quality　prediction　demonstrate　that　RFHGN　outperforms　state-of-the-art　baselines.