Abstract: During the design and use of flight vehicles, to accurately infer the complete aerodynamic distributed loads from sparse surface measurements, it is essential to deploy the limited sensors in locations where encompass abundant information. To address this issue, we propose an efficient scheme based on clustered statistical features. Firstly, statistical features of the aerodynamic distributed loads at all candidate sensor locations are described through four statistical moments, i.e. mean, standard deviation, skewness and kurtosis. Secondly, the K-means++ clustering algorithm is employed to analyze the statistical features at each sensor location. Based on the clustered results, the sensor locations are determined by selecting the location closest to the center of each cluster. Finally, case studies on surface pressure sensor placement for the NACA0012 airfoil and CRM aircraft are used to validate the sparse inversion accuracy of the measurement points obtained by the proposed method. Analysis and comparison of the computational time complexity of each algorithm demonstrate that the proposed method significantly improves sensor placement efficiency compared to optimization-based approaches.