Abstract: Aiming at the low efficiency and instability of traditional industrial robot trajectory planning, a particle swarm optimization algorithm (PSO) that can dynamically adjust learning factors is proposed. This method uses piecewise polynomial interpolation to fit the trajectory, and uses an improved particle swarm algorithm to optimize the trajectory of industrial robots with time as a fitness function. This method effectively combines the piecewise polynomial interpolation function with PSO, avoids the complex process of particle swarm algorithm to construct the adaptation function, and improves the problem that the traditional PSO is more likely to fall into the local extreme value in the early stage and the convergence speed is slow in the later stage. Through experiments, the motion posture, velocity and acceleration trajectories of each joint of the manipulator are known. This method can effectively achieve the optimization of the trajectory of the industrial robot, and ensure the stability of the overall operation while improving the operating efficiency.