Aiming at the issue of high end-to-end latency and the inaccurate of simulation strategy because of the large synchronization delay in 5G power virtual private optical network slicing, a latency optimization algorithm based on deep reinforcement learning is proposed. Firstly, a 5G power virtual private optical network slicing system model is established, which includes a synchronization node for network element updates and other service nodes, where the synchronization node is directly connected to the software defined network controller through a dedicated special fiber. Then, an optimization problem is proposed to minimize the total latency, including business and network element update latency. As this problem involves discrete and continuous variables, both discrete and continuous deep reinforcement learning algorithms were employed for solution. Simulation results show that the proposed algorithm can effectively reduce the latency of the power virtual private optical network slicing network, meet the requirements of service quality, and ensure the real-time performance of the simulation strategy.