针对复杂风场下飞行姿态预测面临的标注样本稀缺、风场与姿态耦合复杂等问题，本文提出一种基于辅助回归生成对抗网络的无监督回归预测方法ARWGAN (Auxiliary Regression Wasserstein Generative Adversarial Network)。该方法的生成器通过融合仿真俯仰角与高斯噪声，生成物理意义一致的风场样本；判别器采用真伪分类与俯仰角回归双分支结构，结合对抗损失与Huber回归损失完成联合优化，无需训练标签即可实现飞行纵向姿态回归预测。实验表明，所提方法在无监督条件下的预测性能（MSE=34.28，MAE=4.87）接近有监督模型，显著优于随机基准方法，且生成样本与仿真样本视觉相似度高。这表明该方法能有效捕捉风场与飞行姿态的非线性关联，为标签稀缺下的无监督回归任务提供了一种新的技术路径。

To address the scarcity of labeled samples and complex coupling between wind fields and flight attitudes in flight attitude prediction under complex wind fields, this paper proposes an unsupervised regression prediction method based on the Auxiliary Regression Generative Adversarial Network (ARWGAN). In this method, the generator fuses simulated pitch angles and Gaussian noise to generate wind field samples with consistent physical meaning; the discriminator adopts a dual-branch structure of authenticity classification and pitch angle regression, and combines adversarial loss with Huber regression loss for joint optimization, enabling flight attitude prediction without training labels. Experiments show that the proposed method achieves prediction performance (MSE=34.28, MAE=4.87) under unsupervised conditions, which is close to that of supervised models and significantly better than random benchmark methods. Additionally, the generated samples have high visual similarity to simulated samples, indicating that the method can effectively capture the nonlinear correlation between wind fields and flight attitudes, providing a new technical approach for unsupervised regression tasks with scarce labels and imbalanced distributions.