With the expansion of high dimensional space in UAV trajectory planning, the flying environment of UAV is very complex, and the external threat of UAV is no longer a simple two-dimensional static threat. The traditional ant colony algorithm and artificial potential field algorithm cannot meet the requirements of real-time and high complex environment. To solve the above problems, a new dynamic programming algorithm based on dynamic weighted A^* algorithm is proposed. Firstly, the flight environment of UAV is modeled. By studying the constraints such as the turning radius, the length of track section and the limit of the maximum range, the safe flight of UAV is ensured, thus reducing the crash rate and the threat probability. Secondly, a new navigation mode is designed by studying the track and external threat parameters of the UAV, which can reduce the danger of navigation and reduce the loss. Then, the potential energy of the vertex can be expanded. The dynamic weight of the overall change of location and grid graph is obtained, and the cost function in dynamic environment is obtained, which increases the speed, accuracy and evasion degree of obstacle avoidance search. Finally, the simulation results show that under the same application environment, the proposed algorithm has the best path, the least threat cost and the shortest execution time compared with the ant colony algorithm and artificial potential field algorithm. To sum up, the dynamic weighted A^* algorithm can be well applied to UAV trajectory planning, reducing the cost of UAV track, shortening the completion time of the algorithm and improving the search speed and precision of unmanned aerial vehicle trajectory planning in complex environment.