To optimize the trajectory of multi-UAV cooperative search for multiple moving targets,an information environment model based on search probability graph is established,and an Artificial Potential Field based Adaptive Particle Swarm Optimization (APF-APSO) search algorithm with adaptive parameter adjustment is proposed for dynamic target search in uncertain environment.In artificial potential field,the virtual repulsive force between the UAV and mountains and between UAVs is used to avoid obstacles effectively,and the virtual attraction force between the UAV and the target is used to speed up the search for targets.Through the nonlinear exponential function parameter adjustment method,the particle swarm parameters are adjusted,and the search probability graph is updated in real time according to the raster cell information certainty and target existence probability obtained by UAV in the search process,so as to guide the UAV to search the target.The simulation results show that compared with other algorithms,the proposed algorithm has great advantages in searching the target and shortens the length of the path.It avoids falling into local optimal solution and has good convergence.It can effectively realize the cooperative search among multiple UAVs and improve the search efficiency.