To meet the requirement of humanoid motion planning of the robotic arm in human-robot interaction environment, a humanoid motion planning method of the robotic arm based on reinforcement learning is proposed in this article. Firstly, based on the structural characteristics of the human arm, the shoulder angle, the elbow angle and the wrist joint motion angle are designed to reflect motion characteristics of the robotic arm. The motion data of human arm captured by the VICON system are analyzed by using normality and correlation analysis methods to achieve the motion characteristics of the human arm. Then, according to different motion characteristics rules, the corresponding reward functions are designed, and the humanoid motion model is trained by the reinforcement learning method. Finally, the humanoid motion platform of the robot arm is established, and the success rate of the humanoid motion is 91. 25% . It evaluates the feasibility and effectiveness of the proposed method, which could be used to improve the humanization of robot motion.