In view of the output uncertainty and intermittent problems of distributed power generation equipment in microgrid, and the shortcomings of traditional deep deterministic policy gradient algorithm, such as slow convergence speed, poor robustness, and easy to fall into local optimum. In this paper, a deep deterministic policy gradient algorithm based on prioritized experience replay is proposed, aiming at the lowest operating cost of the microgrid system, to realize the energy optimal scheduling of the microgrid. First, the Markov decision process is used to model the microgrid optimization problem; secondly, the prioritized experience replay pool with Sumtree structure is used to improve the efficiency of sample utilization, and importance sampling is applied to improve the influence of state distribution on the convergence results. Finally, this paper uses real power data for simulation verification. The results show that the proposed optimal scheduling algorithm can effectively learn the operation strategy that minimizes the economic cost of the microgrid system. At the same time, the introduction of prioritized experience replay and importance sampling improves the performance of the algorithm.