For solving the problem of spectral shift between the source domain and target domain in cross-scene hyperspectral remote sensing image classification， this study proposes a cross-scene hyperspectral image classification model combining spatial-spectral domain adaptation and Xtreme Gradient Boosting （XGBoost）. First， the Depth Over Parametric Convolution Model （DOCM） and Large Kernel Attention （LKA） was combined to form a spatial-spectral attention model and extract the spatial-spectral features of the source domain. Next， the same spatialspectral attention model was used to extract features from the target domain， and the discriminator was used to adapt to the confrontation domain to reduce the spectral shift between the source and target domains. Second， the feature extractor of the target domain was adapted to the supervised domain through a small amount of labeled data in the target domain such that the feature extractor of the target domain can learn the true distribution of the target domain and map the features of the source and target domains to form a similar spatial distribution and complete the clustering domain adaptation. Finally， the ensemble classifier XGBoost was used to classify hyperspectral images to further improve the training speed and confidence of the model. Experimental results for the Pavia and Indiana hyperspectral datasets indicate that the overall classification accuracy of this algorithm reaches 91.62% and 65.98%， respectively. Compared with other cross-scene hyperspectral image classification models， the proposed model has a higher classification accuracy.