Abstract: The present invention provides a self-powered integrated sensing and communication (ISAC) interactive method of high-speed railway based on hierarchical deep reinforcement learning (HDRL), including: Constructing an integrated system framework for passive sensing and communication of high-speed train, where the passive sensor is mainly used for receiving train status information, and the access point (AP) is utilized for status information sensing of the train; During the remote communication between the AP and the base station (BS), Gaussian mixture model (GMM) clustering method is utilized for obtaining reference handover triggering points and completing the communication handover; Proposing an option-based HDRL algorithm to train the high-speed train agent so as to implement the dynamic autonomous switching process of information sensing and remote communication, thereby ensuring the minimum of task completion time and the timely charging for sensors.