The resource allocation problem for Unmanned Aerial Vehicle (UAV) swarms in complex, dynamic environments holds significant research value. We propose a dynamic resource allocation method for large-scale UAV swarms based on Mean Field Game (MFG), addressing resource competition and cooperation challenges in a resupply scenario. By modeling the drones’ positions, resource demands, and movement actions, we design a joint reward function to optimize resource utilization, reduce regional congestion, and minimize movement costs. Major agents are responsible for maintaining global resource balance, while minor agents make locally optimal decisions through distributed strategies. We employ the policy iteration algorithm to solve the Nash equilibrium and verify the method's effectiveness through simulation experiments. The experimental results demonstrate that the proposed method outperforms traditional approaches in terms of resource allocation fairness, total system reward, and convergence speed, offering theoretical support for the autonomous coordination of large-scale UAV swarms.