Data-driven generalizable aerodynamic analysis models demonstrate strong capability in performing real-time reliable aerodynamic analysis for any geometry under arbitrary aerodynamic conditions. It represents a key technology for achieving high-speed intelligent optimization design of aircraft. However, the construction of high generalizable analysis models for complex aerodynamic geometry remains severely constrained by the curse of dimensionality, which necessitates large number of training data, thereby impeding practical implementation and broader application. This paper presents two studies in data-driven airfoil and wing optimization design. By establishing a rational parametric characterization of the aerodynamic shape design space, the adverse effects of the curse of dimensionality were effectively circumvented. A data-driven aerodynamic analysis model with demonstrable generalizability was constructed utilizing approximately 100,000-scale CFD training datasets, enabling high-efficiency optimization design of relevant aerodynamic shapes. As a summary of the author"s preliminary work, this paper aims to inspire the academic community to conduct further research in the field of data-driven aerodynamic optimization design and to promote the advancement of intelligence in the field of aircraft design.