In order to solve the UAV cooperative operation problem, multi-tasks need to be assigned to multiple UAVs. Wireless ultraviolet light is used to realize the covert information transmission between UAVs under the strong electromagnetic interference environment, and an improved particle swarm algorithm for multi-UAV task allocation is proposed, which takes into account the threat cost, voyage cost, and the time difference of completing the task for UAVs to perform the task, and combines the compression factor and the differential evolution idea to solve the problem that particle swarm optimization algorithm is easy to fall into the local optimum. Simulation results show that the improved particle swarm algorithm improves the average success rate of task allocation by about 16% compared with the traditional particle swarm algorithm under different ratios of UAVs and number of tasks, reduces the number of iterations of the algorithm by about 4.5 times on average at the time of convergence, and the optimal fitness value decreases by nearly double on average, which is of some significance in the practical application of multi-UAV task allocation.