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This limitation results in an insufficient representation of travel patterns and associated knowledge. To address these shortcomings, this paper proposes a novel approach that integrates reinforcement learning with knowledge graphs to enable a holistic fusion of heterogeneous data. The proposed method enriches the environmental variables within reinforcement learning frameworks and introduces collaborative updating mechanisms for representing passenger-station interactions based on human travel knowledge graphs. These enhanced representations are then employed to improve the accuracy of next-station prediction. To evaluate the effectiveness of the approach, ablation experiments and comparative analyses with classical algorithms were conducted. 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