The existing domain adaptation methods overly focus on fine-grained feature learning in the source domain， hindering their ability to extend to the target domain effectively， making them prone to overfitting in specific environments， and lacking robustness to complex environments. To address the above mentioned issues， a domain adaptation model that integrates Environment Label Smoothing and Nuclear norm Discrepancy （ELSND） was proposed. In the proposed model， through the environment label smoothing module， the probability of true labels was reduced and the probability of non-true labels was increased to enhance the model adaptability to different scenarios. At the same time， the nuclear norm discrepancy module was employed to measure distribution difference between the source and target domains， thereby improving the classification certainty at decision boundaries. Large number of experiments were conducted on adaptive benchmark datasets of three domains including Office-31， Office-Home and MiniDomainNet. Compared with the state-of-the-art baseline model DomainAdaptor-Aug （DomainAdaptor with generalized entropy minimization-Augmentation） on MiniDomainNet dataset， ELSND model achieves a 1.23 percentage points increase in accuracy of image classification domain adaptation tasks. Therefore， the proposed model has a higher precision and generalization in image classification.