Abstract: Due to the limitation of the equipment computing capacity and the energy resources, UAVs cannot perform intensive computing tasks well. In order to solve this problem, this paper proposes an unloading strategy based on the game theory and the deep learning. This strategy establishes the cooperative games between the unmanned aerial vehicles (UAV), so the minimized cost function is defined as the energy costs and delays, and the existence of at least one Nash equilibrium (NE) is proved. A distributed algorithm for solving the NE of both the game sides is proposed. On this basis, through the reinforcement learning method based on the theory of SLA, the UAVs effectively realizes the implementation of the edge server option. Simulation results show that compared with the other unloading strategies, the unloading mechanism proposed in this paper has a significant effect on reducing the UAV energy consumption, the system costs and the network delays