Aiming at the problems that the basic particle swarm optimization (PSO) Algorithm is fast in convergence and easy to premature maturity, and is prone to fall into local misunderstandings, this paper proposes a particle swarm-artificial bee swarm hybrid (PSO-ABC) algorithm, which is applied to path planning in the three-dimensional of UAV. Based on the improved PSO, the algorithm integrates the ABC algorithm to plan globally the three-dimensional path of UAV. First, the nonlinear inertia weight and shrinkage factor are introduced to improve the particle velocity formula, and then the search operator of the ABC is used to search for the optimal solution again, which solves the problem that the PSO algorithm falls into a local misunderstanding due to its poor local search ability. In this paper, two groups of experiments are set up in a three-dimensional environment to compare the path optimization performance of PSO-ABC algorithm, PSO and ABC algorithm. The experimental results show that the path optimization ability of the algorithm proposed in this paper has been improved, which is 6.1% higher than that of the PSO, and 6.9% higher than that of the ABC.