Aiming at the problems of trajectory planning difficulty, insufficient real-time performance and difficulty in realizing six-degree-of-freedom grasping of existing manipulator dynamic target grasping methods, a manipulator dynamic target grasping method based on deep reinforcement learning (DRL) is proposed. The Markov decision process (MDP) is modeled, and the state space, action space and reward function are designed to realize the six-degree-of-freedom grasping of the dynamic target by the manipulator. Based on Pybullet, the dynamic target grasping simulation test environment of manipulator is constructed, and the method is trained. The trained strategy is tested in a novel scene, and compared with the dynamic target grasping method of classical planning control. The simulation results show that the method can realize the six-degree-of-freedom grasping of the dynamic target by the manipulator, and has advantages in grasping success rate and speed.