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Cotton is an important economic crop and strategic reserve material in China, timely and accurate acquisition of cotton spatial distribution information is of great significance for cotton yield prediction and agricultural policy development and adjustment. In order to address the problems of the difficult availability of high-resolution remote sensing data and insufficient usability of feature information by traditional machine learning, a CBAM-U-HRNet classification model was established to extract cotton planted area, where U-HRNet and CBAM attention mechanism were combined, and Tumxuk City in the southern Xinjiang was taken as an study area. Firstly, the Sentinel-2 remote sensing data were pre-processed and annotated. Secondly, the attention mechanism CBAM was introduced into U-HRNet to enhance the important features for cotton classification, suppress the relatively unimportant features, and reduce the interference caused by complex background information. Finally, U-Net, HRNet and U-HRNet were selected to compare with CBAM-U-HRNet model to test their performance in the classification of cotton planted area. During this process, two different spatial resolution datasets such as Sentinel-2 (10m) and GF-2 (1m) were used, and the advantages of CBAM-U-HRNet model were evaluated by using the best feature subset. The results showed the CBAM-U-HRNet model that using Sentinel-2 remote sensing data had the best classification accuracy for cotton planted area, with mIoU and mPA reaching 92.78% and 95.32%, respectively. Comparing with the Sentinel-2 dataset, the GF-2 data had higher spatial resolution and achieved higher accuracy by using HRNet, U-Net and U-HRNet networks. For the two datasets with different spatial resolutions, the classification accuracies of cotton planted area using the CBAM-U-HRNet model was comparable to each other. The CBAM-U-HRNet model can reduce the misclassification induced by the difference in spatial resolution of the two datasets. Comparing with the random forest algorithm, the CBAM-U-HRNet model had higher accuracy in the classification of cotton. The research results can provide technical support for the classification of cotton, and the fast and objective extraction of vegetation planted area in arid regions.