Least squares regression (LSR) is a fundamental statistical analysis technique that has been widely applied to feature learning. However, limited by its simplicity, the local structure of data is easy to neglect, and many methods have considered using orthogonal constraint for preserving more local information. Another major drawback of LSR is that the loss function between soft regression results and hard target values cannot precisely reflect the classification ability; thus, the idea of the large margin constraint is put forward. As a consequence, we pay attention to the concepts of large margin and orthogonal constraint to propose a novel algorithm, orthogonal least squares regression with large margin (OLSLM), for multiclass classification in this letter. The core task of this algorithm is to learn regression targets from data and an orthogonal transformation matrix simultaneously such that the proposed model not only ensures every data point can be correctly classified with a large margin than conventional least squares regression, but also can preserve more local data structure information in the subspace. Our efficient optimization method for solving the large margin constraint and orthogonal constraint iteratively proved to be convergent in both theory and practice. We also apply the large margin constraint in the process of generating a sparse learning model for feature selection via joint -norm minimization on both loss function and regularization terms. Experimental results validate that our method performs better than state-of-the-art methods on various real-world data sets.