The Dynamic Window Approach (DWA) algorithm initially computes traversable areas using a kinematics model and subsequently selects the optimal trajectory based on an evaluation function, reward (ξ). This mode has emerged as a prevalent solution for decision-making in autonomous driving. However, the traditional DWA method employs a fixed evaluation function across all scenarios, indicating that it uses the same decision-making logic regardless of any driving situation, which is obviously unreasonable. In this paper, we propose an adaptive-weight DWA method based on imitation learning. The proposed method encompasses an algorithmic workflow that includes candidate trajectory generation, feature extraction, a trained model, and evaluation of optimal trajectories. Additionally, the paper presents the AdaptiveWeightNet architecture, which dynamically generates evaluation function weights tailored to the current driving scenario through encoding and computation of the scenario. These weights, combined with the generated candidate trajectory features, enable adaptive adjustment of decision-making logic to select the optimal trajectory. Our proposed method has demonstrated promising results on a self-constructed dataset. When compared to the fixed-weight results obtained using the Maximum Entropy Inverse Reinforcement Learning method, our improved algorithm exhibits substantial improvements in scene adaptability, driving stability, and similarity to human driving behavior.