Multiple kernel learning shows good superiority in solving irregular and large-scale data problems. Regularization path is a method to select the optimal model by solving the multiple kernel learning multiple times.Aiming at the problems that the kernel matrix size is large， the computational cost is high and the efficiency of the optimization model is affected when multiple kernel learning regularization path processes large-scale data， a multiple kernel learning regularization path approximation algorithm based on CUR matrix decomposition is proposed, which is named MKLRPCUR.This algorithm firstly adopts CUR algorithm to obtain multiple decomposition matrices of low-rank opproximation matrix of kernel matrix.Then， in the solution process， the low-dimensional decomposition matrices are used to replace the kernel matrix， and the order of the correlation matrix calculation is adjusted， thereby simplifying the calculation of the kernel matrix and the Lagrange multiplier vector product.MKLRPCUR algorithm reduces the calculation scale of matrix， optimizes matrix calculation， and improves the calculation efficiency of exact algorithm.The relative error of the low-rank approximation matrix and the time complexity of the algorithm are theoretically analyzed to verify the rationality of the approximation algorithm.At the same time， the experimental results on the UCI dataset， ORL and COIL image databases show that the proposed approximate algorithm not only ensures the accuracy of learning， but also reduces the running time of the algorithm and improves the efficiency of the model.