In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. Air combat engagement database (ACED) is a dedicated database for researching WVR air combat. Utilizing the data in ACED, a Transformer-based BFM decision support scheme is developed to enhance the pilot’s BFM decision making in WVR air combat. The proposed Transformer-based model significantly outperforms the baseline long short-term memory (LSTM)-based model in accuracy. To augment the interpretability of this approach, Shapley Additive Explanation (SHAP) analysis is employed, exhibiting the rationality of the Transformer-based model’s decisions. Furthermore, this study establishes a comprehensive framework for evaluating air combat performance, validated through the utilization of data from ACED. The application of the framework in WVR air combat experiments shows that the Transformer-based model increases the winning rate in combat from 30% to 70%, the average percentage of tactical advantage time from 4.81% to 14.73%, and the average situational advantage time share from 17.83% to 25.19%, which substantially improves air combat performance, thereby validating its effectiveness and applicability in WVR air combat scenarios.