This paper proposes a task allocation algorithm based on the combination of reinforcement learning and deep neural networks to address the problem of multi-UAV cooperative multi-objective task allocation. It utilizes graph neural networks (GNN) and attention mechanisms to model the policy function, thereby constructing a task allocation strategy based on the collective state of the UAV swarm which enables the reinforcement learning algorithm to generalize to varying numbers of enemy target nodes in the environment. To improve the efficiency and stability of training, the S-sample batch reinforcement learning algorithm is adopted. The simulation results demonstrate that the algorithm can effectively solve the multi-UAV task allocation problem.