In order to improve the analysis and optimization efficiency of stiffened composite cylindrical shells, a novel fast modeling method for the augmented radial basis function (ARBF) approximate model is proposed by fusing sam-ple clustering and improved K-fold crross validation.The K-means clustering algorithm is used to determine the optimal sample clustering, and the reference shape parameters of the samples are determined based on the local density of the samples. The scaling coefficient is introduced into each cluster to adjust the shape parameters adap-tively, which effectively taking into account the efficiency and accuracy of optimizing the shape parameters.Then, the evaluation criteria of ARBF approximation model generalization performance based on bias-variance decom-position is established, which solves the problem of insufficient utilization of traditional K-fold cross-validation sample information. Based on the block matrix inversion technique, a fast inversion method of the high-order coef-ficient matrix of the ARBF auxiliary approximation model is derived, and the scaling coefficient optimization method based on the improved K-fold cross-validation is proposed, which greatly reduces the computational complexity of determining the optimal shape parameters and improves the efficiency and accuracy of ARBF approximation mod-eling.Numerical and engineering examples show that the optimal sample clustering and fast cross-validation have significant gains on the approximate modeling efficiency and accuracy, and reduce the sensitivity of modeling effi-ciency to sample size and problem dimension. Moreover, under the same number of training samples, the model-ing accuracy of the proposed method is significantly superior than that of other typical methods, which verifies the effectiveness and advancement of the method and has certain engineering application value.