To enhance the intelligence and scheduling efficiency of rail-guided logistics systems, a path planning system integrating digital twin technology with reinforcement learning algorithms was proposed. First, a hierarchically structured and regionally segmented control system architecture was designed. By adopting a hybrid control network based on “regional division and hierarchical management,” the system enables efficient collaborative control of logistics robots and track-side facilities. Second, a “four-layer, five-space” digital twin architecture conforming to the ISO 23247 standard was constructed to ensure high real-time responsiveness and consistency between the physical system and its virtual counterpart. To address path conflicts among multiple robots and improve planning efficiency, ID3QN (improved double deep Q-network) algorithm was proposed. This algorithm introduces a three-dimensional reward mechanism—comprising safety, traffic efficiency, and timeliness—along with an action pruning strategy, significantly enhancing the convergence and stability of path planning. Experimental results demonstrate that the proposed system exhibits excellent scheduling performance in complex and dynamic environments, reducing the average robot path time by 9.1% and lowering the conflict rate by 12.3%. Relevant simulation and experimental testing have validated the feasibility of the proposed method in practical rail-guided logistics scenarios, demonstrating its application value in enhancing the intelligence level of rail-guided logistics systems.