An improved particle swarm optimization (FPSO) algorithm is proposed, which makes particles focus on different optimization tasks according to the fitness value, and applies it to the UAV 3D path planning problem. The traditional particle swarm optimization (PSO) algorithm sets uniform control parameters for all particles, the optimization process is not flexible enough, it is easy to fall into local extremes and the convergence speed is slow. The improved FPSO algorithm proposes three optimization strategies, that is, the combination of PSO algorithm and genetic algorithm (GA), setting dynamic inertia weight and introducing step factor, so as to give full play to the search advantages of particles with different fitness values and make them dynamically focus on local search or global search. The simulation results show that FPSO algorithm has better search results, fewer iterations, and the average consumption time is 22.0% shorter than PSO algorithm and 39.6% shorter than GA algorithm.