This　research　aims　to　explore　the　complex　dynamics　governing　the　correlation　between　oil　futures　prices　and　Chinese　agricultural　futures　prices,　with　a　specific　emphasis　on　unveiling　the　crucial　role　played　by　oil　futures　prices　in　predicting　the　trajectory　of　agricultural　futures　prices.　The　study　utilizes　the　Vector　Error　Correction-Dynamic　Conditional　Correlation-Multivariate　Generalized　Autoregressive　Conditional　Heteroskedasticity　(VEC-DCC-MGARCH)　model　to　dissect　the　interplay　among　oil,　soybean,　and　corn　price　series.　Additionally,　this　study　integrates　the　innovative　Spatio-temporal　Information　Recombination　Hypergraph　Neural　Network　(STIR-HGNN)　model　to　analyze　how　oil　futures　prices　contribute　to　improving　the　accuracy　of　forecasting　agricultural　product　prices.　Findings　indicate　numerous　connections　between　oil　prices　and　agricultural　futures　prices,　highlighting　the　significant　role　of　oil　prices　in　forecasting　agricultural　futures　price　movements.　The　empirical　insights　derived　from　this　study　serve　as　a　valuable　compass　for　futures　market　participants,　urging　them　to　leverage　these　findings　to　refine　and　optimize　their　market　strategies,　enhancing　their　capacity　to　navigate　and　capitalize　on　the　intricate　complexities　inherent　in　these　interconnected　markets.