Abstract: Unsupervised domain adaptation aims to transfer knowledge from labeled data in the source domain to the unlabeled target domain. The unsupervised domain adaptation method based on domain alignment learns domain invariant features by minimizing the difference in cross domain feature distribution, ignoring the learning of discriminative features in the target domain, resulting in poor discriminability of the learned domain invariant features in the target domain, confusion of different types of features, and performance decrease of the model. To address the problem of poor discriminative power of domain invariant features in the target domain during domain alignment, this paper proposes a deep Subdomain Adaptive method based on domain invariant Discriminative Feature learning (DFSA) . The deep subdomain adaptive method is combined with the constraints of minimizing class confusion to increase the inter-class differences of domain invariant features, and consistency regularization is added to reduce the intra-class differences of domain invariant features. By doing so, domain invariant discriminative features are mined to better preserve the discriminative power of domain invariant features in the target domain and improve domain adaptability. The effectiveness of DFSA was validated on the Office-31 and Office-Home benchmark datasets, and the results showed that DFSA effectively improves domain adaptation performance, with an average accuracy improvement of approximately 0.79% and 3.51%, respectively, when compared with existing deep subdomain adaptation methods on the two benchmark datasets.