The existence of adversarial examples can easily mislead deep learning models into making incorrect predictions, severely affecting the robustness of these models. To enhance the robustness of models and resist interference from adversarial samples, a domain adaptive robust image classification method was proposed. Firstly, by analyzing the distribution characteristics of clean images and adversarial examples, domain adaptation learning methods were used to align the feature spaces of clean images and adversarial examples. Secondly, clean images and adversarial examples were treated as the source domain and target domain respectively, to construct a generative adversarial classification network. Finally, an adversarial learning linear loss function was constructed to optimize the network. The proposed algorithm, validated with adversarial samples constrained by l_∞ and l₂ norms, demonstrates an improvement of 4.3% and 1.23% in standard accuracy compared to standard training and adversarial training algorithms on the MNIST-M dataset, and an improvement of 1.23% and 20.45% on the CIFAR-10 dataset. Meanwhile, the robust accuracy on three types of adversarial samples is increased by more than 10%. The robust accuracy on the remote sensing scene classification SIRI-WHU dataset reaches 79.6%. Experimental results indicate that the proposed algorithm effectively enhances the standard accuracy and robust accuracy of image classification models, demonstrating stronger robustness when facing adversarial sample perturbations.