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Forecasting pedestrians' future trajectory in unknown complex environments is essential to autonomous navigation in real‐world applications, for example, for self‐driving cars and collision warnings. However, modern observed trajectory‐based prediction methods may easily over‐fit to complex or rare scenes because they do not entirely understand the correlations between scenes and trajectories. To address the over‐fitting problem, an Inverse Reinforcement Learning for Scene‐oriented Trajectory Prediction (IRLSOT) is proposed in this work. The authors' method can be divided into three modules. First, the inverse reinforcement learning module generates the optimal policy by extracting features from scenes and pedestrians' observed trajectories. A lightweight ENet is used to extract features from scenes. Afterwards, the path sampling module introduces a Gumbel Softmax Trick (GST) to improve the accuracy of optimal policy sampling. Different paths are generated on the basis of the optimal policies. Finally, the information fusion module uses the proposed Scene Based Attention (SBA) to fuse the path and trajectory information, then outputs the predicted trajectories. Comparison results show that IRLSOT improves performance on Stanford Drone Database(SDD) by 5.9%$\%$. Furthermore, the authors' test IRLSOT on multi‐agent scenarios and the authors' own data sets, and results demonstrate that IRLSOT can enhance the generalization of trajectory prediction to rare or new scenes. [ABSTRACT FROM AUTHOR]