Abstract: The Bayesian optimization framework has the characteristics of high optimization efficiency and good optimization effect, which is suitable for solving high-dimensional black-box optimization problems, i.e., in the field of airfoil design. However, due to the opacity of the optimization process, it is difficult to intuitively understand the relationship between the machine optimization results and typical physical characteristics of the airfoil. Therefore, how to interpret the Bayesian optimization process is still a challenge. To solve this problem, an airfoil optimization interpretability method based on the sparse Bayesian optimization framework is proposed, which utilizes typical geometric features of physical significance in the optimization process. During the process of Bayesian optimization, the airfoil features are sparse, and interpretability information is obtained. The proposed method is verified using the example of supercritical airfoil optimization with RAE2822 as the reference airfoil. The experimental results show that the proposed method reduces the airfoil design dimension as much as possible, and makes the data explainable to a certain extent while ensuring good aerodynamic performance, which can help intuitively understand the influence degree of airfoil parameters on the optimization objective and assist designers to make decision and judgment during the airfoil design.