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A dynamic path planning algorithm based on deep reinforcement learning is proposed in order to use mobile robots to carry goods or perform manned performances in complex stage environment. Firstly, the obstacle information around the mobile robot is obtained by constructing a global map, and the actors and stage props are classified into dynamic obstacles and static obstacles respectively. Then establish a local map, encode the dynamic obstacle information through LSTM network, and calculate the importance of each dynamic obstacle through social attention mechanism to achieve better obstacle avoidance effect. By constructing a new reward function, different avoidance situations of dynamic and static obstacles are realized. Finally, simulation learning and priority experience playback technology are used to improve the convergence speed of the network, so as to realize the dynamic path planning of mobile robot in the complex stage environment. The experimental results show that the convergence speed of the network is significantly improved, and it can show good dynamic obstacle avoidance effect in different obstacle environments.