Aiming at the task assignment problem of UAV swarm target strike, this paper proposes an intelligent UAV task assignment method based on reinforcement learning. This strategy proposes a task layering framework, which treats multiple UAVs as an alliance and classifies the targets to form task clusters, maps each task cluster to the UAV alliance. Through multi-agent reinforcement learning algorithm (MADDPG), the targets in the task cluster are reasonably paired with the small UAVs in the UAV alliance, then the targets are hit. The return value and flight path of MADDPG algorithm are obtained, and compared with the return value and flight path of DDPG algorithm and DQN algorithm. The experimental results show that in the task assignment of small samples, compared with the non-hierarchical method, this method can improve the completion degree of target task strike and improve the efficiency of target strike; under the hierarchical framework, compared with the other two algorithms, the convergence speed is faster, the convergence process is more stable.