To solve the UAV path planning problem, a method based on Fusion of Simplified Sparse A^* algorithm and Simulated Annealing algorithm (FSSA-SA) is proposed. Firstly, after modeling the threat environment, the simulated annealing idea is combined with the solution of the specific route planning problem, and the concrete design and implementation method of the simulated annealing algorithm is given. Secondly, the simplified sparse A^* algorithm is used to search the roundtrip tracks between the start point and the end point, and the better one of the results will be used as the initial solution of the simulated annealing algorithm to realize the fusion of the two algorithms. Then, when annealing proceeds to the low temperature region, the solution quality of the algorithm is further improved by eliminating the redundant track nodes. Finally, in order to verify the superiority of the proposed algorithm, the simulation experiments are carried out with sparse A^* algorithm and simulated annealing algorithm. The experimental results show that the proposed FSSA-SA algorithm has less planning time-consuming than the two algorithms mentioned above; compared with the sparse A^* algorithm, the memory occupied by the FSSA-SA algorithm is two orders of magnitude less when the synthetic cost of the obtained track is not too different; compared with the simulated annealing algorithm, under the same annealing conditions, the integrated cost of the planned track is reduced by about 35% on average.