In complex adversarial air defense operations， the identification of battlefield target intent has become crucial for situational awareness. To address the characteristics of flexible state variations and strong regularity in the intent of formation targets， this paper proposes a data-knowledge dual-driven method for formation target intent recognition. First， a hybrid framework of data-knowledge dual-driven intent recognition is designed to address the limitations of using either approach alone. Subsequently， based on this framework， a data-driven Target State Prediction （BC-TSP） method is proposed to solve the problem of insufficient knowledge-driven global search capability， combining Bidirectional Long Short-Term Memory （BiLSTM） and Conditional Generative Adversarial Network （CGAN）. Furthermore， a knowledge-driven intent inference method based on fuzzy decision trees is introduced to solve the problem of data-driven solution space explosion and low interpretability. Comprehensive experimental validation demonstrates that the proposed method not only exhibits strong temporal feature learning capability， enabling accurate prediction of future formation target states， but also efficiently leverages incomplete prior knowledge through a highly interpretable reasoning process to determine the final intent of formation targets. This approach satisfies the situational awareness needs of air defense commanders， assisting them in making better-informed tactical decisions.