For the trajectory planning of multi-stage continuum robots, a trajectory planning algorithm based on deep deterministic policy gradient reinforcement learning is proposed. Firstly, based on the piecewise constant curvature hypothesis, the forward velocity kinematic model of joint angular velocity and end pose of the continuum robot is established. Then, the reinforcement learning algorithm is used to take the current pose and target pose of the robot arm as state input, the joint angular velocity of the robot arm as the output action of the agent, and a reasonable reward function is set to guide the robot to move from the initial pose to the target pose. Finally, a simulation system is built in MATLAB, and the simulation results show that the reinforcement learning algorithm successfully performs trajectory planning for the multi-segment continuum robot and controls the end of the continuum robot to move smoothly to the target pose.