Abstract: The data mining & knowledge discovery technology is utilized for the UIUC (University of Illinois at Urbana-Champaign) airfoil database in this study. The CST (class function/shape function transformation) method is used for the parametric geometry representation of airfoils, and those airfoils with false data or deficient geometric data can be identified by comparing the CST-reconstructed profile with the original one. The distribution of the CST parameters is analyzed and appears to be a normal distribution. The correlation analysis of the CST parameters shows that some parameters are weakly correlated. The results of the CST parameter clustering analysis are basically consistent with the airfoil classification in engineering. Moreover, the extremum difference method, SOM (self-organized mapping), and Apriori algorithm are applied for knowledge mining of the influence of CST parameters on the aerodynamic characteristics of airfoils under typical conditions. More specifically, the extremum difference method is used to obtain the significance of each CST parameter affecting the aerodynamic characteristics, while the SOM and Apriori algorithm are applied to analyze the correlation between the CST parameters and the aerodynamic characteristics. Finally, the SVM (support vector machine) and DNN (deep neural network) are separately used to construct prediction models linking the aerodynamic characteristics with the CST parameters under typical operating conditions. It is found that the DNN model performs significantly better than the SVM model in terms of the fitting and generalization capability. These mined knowledge can provide supports for the aerodynamic characteristics analysis and design of engineering airfoils.