Optimizing flow model in trade zones based on multimodal graph neural network

To optimize resource allocation in free trade zones (FTZs), this study proposes a computational synergy mechanism leveraging multimodal network modeling. We integrate five-dimensional data streams—capital flows, logistics networks, human mobility, institutional policies, and digital information—through multimodal feature fusion. Key innovations include: (1) A dynamic graph embedding framework using graph attention networks (GATs) to model cross-factor dependencies; (2) A multi-agent coordination algorithm combining Nash bargaining with proximal policy optimization (PPO) for resource conflict resolution; (3) Real-time resource scheduling via heuristic search and stream processing. Validated with China’s pilot FTZ operational data, cross-border clearance workflows accelerated by 32.4% through dynamic scheduling.