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In order to solve the problems of unstable and low efficiency in the work space, a particle swarm optimization (PSO) algorithm with dynamic changes of inertia weight and learning factors was used to optimize the trajectory of 3-5-3 piecewise polynomial interpolation. Through the kinematic analysis of the forest work vehicle, four passing points in the working space were obtained, and the 3-5-3 piecewise polynomial interpolation was used for trajectory planning. Finally, the improved particle swarm optimization algorithm was used to optimize the trajectory running time under the kinematic constraints, and the optimization performance of the standard particle swarm optimization algorithm was compared. The results showed that the 3-5-3 piecewise polynomial interpolation curve using the improved particle swarm optimization algorithm had faster convergence speed, smaller fitness after convergence, and about 13% shorter running time, which effectively improved the working efficiency of the forest work vehicle. [ABSTRACT FROM AUTHOR]