The conventional large-scale subspace clustering methods ignore the local structure that prevails among the data when computing the anchor affinity matrix， and have large error when calculating the approximate eigenvectors of the Laplacian matrix， which is not conducive to data clustering. Aiming at the above problems， a Large-scale Subspace Clustering algorithm with Local structure learning （LLSC） was proposed. In the proposed algorithm， the local structure learning was embedded into the learning of anchor affinity matrix， which was able to comprehensively use global and local information to mine the subspace structure of data. In addition， inspired by Nonnegative Matrix Factorization （NMF）， an iterative optimization method was designed to simplify the solution of anchor affinity matrix. Then， the mathematical relationship between the anchor affinity matrix and the Laplacian matrix was established according to the Nystr?m approximation method， and the calculation method of the eigenvectors of the Laplacian matrix was modified to improve the clustering performance. Compared to LMVSC （Large-scale Multi-View Subspace Clustering）， SLSR （Scalable Least Square Regression）， LSC-k （Landmark-based Spectral Clustering using k-means）， and k-FSC（k-Factorization Subspace Clustering）， LLSC demonstrates significant improvements on four widely used large-scale datasets. Specifically， on the Pokerhand dataset， the accuracy of LLSC is 28.18 points percentage higher than that of k-FSC. These results confirm the effectiveness of LLSC.