High-speed railway train operation rescheduling is a complex multi-stage sequential decision problem which requires consideration of trains, line equipment and other conditions, and the decision space grows exponentially with the scale of the problem. Deep reinforcement learning(DQN), which combines powerful search and learning capabilities, provides a new solution for high-speed railway rescheduling, but has problems such as inefficient use of experience and poor transferability. This paper proposes a transferable deep reinforcement learning high-speed railway rescheduling method based on prioritized experience replay. The high-speed railway rescheduling problem which contains constraints such as the track utilization plan is constructed as a multi-stage sequential decision-making process, and in order to improve the transferability of the algorithm, a new state vector and action space that supports the sharing of source and target domains is proposed. A deep Q-network training method combining prioritized experience replay for high-speed railway rescheduling is designed to improve the efficiency of experience utilization and the convergence speed of the algorithm. The experience learning experiments using the small-scale cases of Xulan line as the source domain problems show that the experience utilization efficiency and algorithm convergence speed of the proposed algorithm are better than that of the traditional DQN algorithm, and the convergence performance can be improved by appropriately increasing the priority exponent and adjusting the weight parameters. Taking the delay cases in the busy section of Jinghu line as the target domain problems, the online decision making algorithm proposed in this paper reduces the decision time by about 75$%$ on average compared with the classical mixed integer programming algorithm, and the performance loss of the total delay time is within 15$%$ in nearly 77$%$ of the cases.