<p>Precision timing synchronization is of essential significance to achieving valid and credible digital-physical fusion testing for high-speed aerospace applications. The conventional synchronization approaches cannot satisfy accurate synchronization needs over time when working in complex and changing environments because these approaches fail to consider how varying loads, physical field variations, and various real-world impacts affect precision synchronization. The novel clock synchronization method for digital-physical networks based on deep reinforcement learning (RLCSM), introduced here, integrates a clock model to integrate influences from the environment and network through reinforcement learning (RL) training. An RL agent performs error correction learning according to its total residual synchronization error as a reward signal while adapting to the precise demands of different situations. A case study was carried out under complicated and dynamic test scenarios to verify the effectiveness of RLCSM. Compared to traditional synchronization protocols, RLCSM effectively reduces average synchronization error and keeps the microsecond precision level, providing effective error compensation at a microsecond level for synchronization precision with high consistency. The results prove that a learning-based method can achieve successful error control and attains satisfactory levels of flexibility for managing drifting clock phase cycles, despite their complex, non-linear characteristics. Consequently, the proposed method achieves highly precise performance of synchronized clocks under all-encompassing conditions. The algorithm enhances the fusion degree between digital and physical space as well as improving the stability and reliability of digital-physical fusion testing on high-end aerospace equipment.</p>