Hyperspectral image classification is a major task in remote sensing data processing. To solve the problem of inconsistent distribution of labeled source and unlabeled target domains， an unsupervised domain adaptive method based on partial optimal transport is proposed to achieve pixel-level classification of hyperspectral ground objects under different data distributions. Specifically， a deep convolution neural network is used to map the sample to the potential high-dimensional space， and the sample transportation scheme is established based on the partial optimal transport theory to minimize the distribution discrepancy between domains. Class-aware sampling and the mass factor adaptive adjustment strategy are used to promote the class alignment between domains and establish a global optimal transport. Experiments were conducted on two open-source hyperspectral image datasets， and the classification accuracies were compared quantitatively from the three evaluation matrices of overall accuracy （OA， %）， average accuracy （AA， %）， and Kappa （×100）. Compared with the source-only method， the improved classification accuracies with the proposed method for OA and AA were 2.21% and 2.75%， respectively， and compared with the original optimal transport， the improved accuracies were 1.71% and 2.01%， respectively. These results show that the proposed model can effectively improve pixel-level classification accuracy in hyperspectral images.