Decision-making in long-tail scenarios is pivotal to autonomous-driving development, and realistic and challenging simulations play a crucial role in testing safety-critical situations. However, existing open-source datasets lack systematic coverage of long-tail scenes, and lane-change maneuvers being emblematic, rendering such data exceedingly scarce. To bridge this gap, we introduce a data mining framework that exhaustively analyzes two widely used datasets, NGSIM and INTERACTION, to identify sequences marked by hazardous behavior, thereby replenishing these neglected scenarios. Using Generative Adversarial Imitation Learning (GAIL) enhanced with Proximal Policy Optimization (PPO), and enriched by vehicular-environment interaction analytics, our method iteratively refines and parameterizes newly generated trajectories. Distinguished by a rationally adversarial and sensitivity-aware perspective, the approach optimizes the creation of challenging scenes. Experiments show that, compared to unfiltered data and baseline models, our method produces behaviors that are simultaneously both adversarial and natural, judged by collision frequency, acceleration profiles, and lane-change dynamics, offering constructive insights to amplifying long-tailed lane-change instances in datasets and advancing decision-making training.