We propose a new method for multi-class classification and feature selection using the sparse envelope model. The sparse envelope model (Su et al., 2016) can conduct variable selection on the responses in a multivariate regression model and achieve the efficiency gains compared to the standard model. We apply the sparse envelope model to a one-way multivariate analysis of variance which enable it to perform a feature selection in this context. We found that, even though the part of features is not significant, non-selected features should not be removed to improve efficiency of significant features. Simulation studies show that our method has a selection consistency and lower misclassification rates than some recent methods. Consistency and the oracle property of the proposed model are established and the asymptotic distribution of the estimator is obtained.