Abstract: The distribution of source domain samples and target domain samples is quite different in domain adaption. Traditional domain adaption methods often tend to ignore the prior label information of samples when aligning the domain distribution, which leads to the lack of discriminability of subspace samples after projection. To alleviate this problem, a two-stage unsupervised domain adaption methodis proposed.This method uses class label information of samples to obtain the discriminative target subspace, and at the same time imposes block diagonal constraints on the reconstruction matrix, obtains domain invariant features in the cross-domain subspace, and improves the performance of model classification. Experiments on three benchmark datasets commonly used in domain adaptation, and the results show that the proposed method has better classification performance.