A composite dynamic movement primitives algorithm based on Dirichlet process clustering and Gaussian mixture model is proposed to address the problems of low efficiency of parameter estimation and insufficient generalization ability in demonstration learning. To achieve the real-time estimation of Gaussian mixture model parameters, the Dirichlet clustering algorithm based on the distance threshold is used to perform online clustering of demo trajectory points, and the Welford formula is introduced to update the parameters to improve the efficiency of parameter estimation. After obtaining the trajectory distribution characteristics, the Gaussian mixture regression trajectories are encoded by using the dynamic movement primitives to improve the trajectory generalization. To evaluate the effectiveness of the algorithm, trajectory reachability and similarity metrics are introduced to evaluate the learning generalization ability of the algorithm, and demonstration learning experiments based on handwritten letter trajectories and robot kinesthetic demonstrations are designed. Experimental results show that the average parameter estimation time of the proposed composite dynamic movement primitive algorithm is only 0. 052 ms, which has the ability of fast trajectory reproduction and generalization.