To enhance the efficiency and performance of trajectory planning for the robotic arm of a moxibustion robot, a study on joint space trajectory planning methods was conducted. An improved particle swarm optimization (PSO) algorithm was proposed, which incorporated dynamically adjusted inertia weights and learning factors, combined with 3-5-3 polynomial interpolation for trajectory planning. A six-axis robot model in MATLAB was used to establish the robotic arm model for simulation experiments. In the simulations, the proposed algorithm was compared with the standard PSO algorithm. The results showed that the joint angular displacement, angular velocity, and angular acceleration curves planned by the improved algorithm were continuous and smooth without abrupt changes. The initial and final velocities were zero, and the entire velocity and acceleration profiles strictly satisfied the constraints without exceeding the maximum operational limits. Meanwhile, the trajectory planning time was reduced from 7 s to 3.139 s, representing a 55.16% improvement in time efficiency. The results verify the effectiveness and superiority of the proposed algorithm in robotic arm trajectory planning.