In the face of UAV penetration tasks under complex terrain conditions, Particle Swarm Optimization (PSO) is easy to fall into local optimum and suffers from long search time in the process of finding the optimal path.To solve the above problems, the spherical coordinate system is introduced into PSO, and the path obtained is regarded as a vector.The iterative updating of particles is realized through the relationship between the distance, elevation and azimuth of the vector and the speed, pitch and steering angle of the UAV.Finally, the random adaptive inertia weight is introduced to make up for the deficiency of the particles local search ability in the early stage and global search ability in the later stage.The simulation results show that the improved algorithm can effectively avoid the threat region, has faster convergence rate and higher convergence accuracy, and is not easy to fall into local optimum.