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The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi-energy complementary system, the hydropower-photovoltaic-hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra-day scheduling of HPH system brings challenges due to the time-related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)-based data-driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor-critic networks are properly designed based on prior knowledge-based deep neural networks for the considered complex uncertain system to search for near-optimal policies and approximate actor-value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity-hydrogen system to achieve rapid response and more reasonable energy management.