In the path planning of unmanned aerial vehicles (UAVs), the traditional algorithm has the disadvantages of complex computation and slow convergence, while particle swarm optimization (PSO) features simple principle, strong universality, and comprehensive search, which is mainly used in UAV route planning. As the conventional PSO algorithm is easy to fall into the local optimum, this study integrates the global extreme variation and acceleration terms based on the adaptive parameter optimization to balance the global and local search efficiency and avoid the population falling into “premature”. Through the test of a variety of benchmark functions, the results show that the improved PSO algorithm proposed in this study has faster convergence speed and higher convergence accuracy. In the example verification part, the flight scene features are first extracted, and the environment modeling is carried out based on the UAV performance constraints. Then multiple constraints and the expected minimum flight time are converted into penalty functions. With the minimization of penalty functions as the objective, the route planning model is constructed, and the improved PSO algorithm is adopted to solve the problem. Finally, the effectiveness and practicability of the improved PSO algorithm are verified by comparative simulation.