随着高性能计算和人工智能技术的迅猛发展，数据驱动的人工智能模型在民机气动设计领域得到了广泛研究，尤其在气动设计空间压缩、关键特征提取、流场预测和智能优化设计等方面展现出强大的技术潜力。然而，纯数据驱动模型在工程设计中的应用仍然面临诸多挑战，包括领域数据稀缺及高获取成本，以及模型在可靠性、通用性、可解释性和易用性方面的不足等。将物理知识与气动设计经验有机融合到模型开发中，成为解决上述挑战的关键路径，为推动该领域的技术进步提供了重要方向。本文从民机工程设计角度出发，结合智能气动设计的相关实践，回顾了数据与知识联合驱动的人工智能模型在知识嵌入、知识修正及知识挖掘三个方面的最新理论和进展，探讨了数据与知识联合驱动方法在民机气动设计领域的研究现状及应用潜力，并展望了智能气动设计新范式的未来。

With the rapid advancement of high-performance computing and artificial intelligence technologies, data-driven AI models have been extensively researched in the field of civil aircraft aerodynamic design, demonstrating signifi-cant potential in design space compression, key feature extraction, flow field prediction, and intelligent optimiza-tion design. However, the application of purely data-driven models in engineering design still faces many chal-lenges, including the scarcity and high acquisition cost of domain-specific data, as well as deficiencies in model reliability, generality, interpretability, and usability. Integrating physical knowledge and aerodynamic design experi-ence into model development has become a key approach to addressing these challenges, providing an important direction for advancing technology in this field. This paper, from the perspective of civil aircraft engineering design and supported by relevant practices in intelligent aerodynamic design, reviews recent theories and progress in data- and knowledge-driven AI models in the areas of knowledge embedding, knowledge correction, and knowledge discovery. It further explores the current state of research and application potential of data- and knowledge-driven methods in civil aircraft aerodynamic design, while offering insights into the future of new para-digms in intelligent aerodynamic design.