Given the problems of low-quality images and limited quantity of samples in the existing remote sensing image recognition, it is difficult to adequately extract the concealed distinguished features of images by adopting single attention mechanism. In this paper, a method is proposed to detect the region destruction of remote sensing images by integrating attention mechanism and capsule network module. The method begins by super-resolution processing of the raw destruction data using the BSRGAN super-resolution model, and data expansion of the processed images using various data augmentation operations. Then the multi-attention capsule encoder-decoder network MA-CapsNet (multi-attention capsule encoder-decoder network) model proposed in this paper is adopted for further processing. The low-level features are extracted with a cascading attention mechanism consisting of the Swin-transformer and the Convolutional Block Attention Module (CBAM). Finally, the CapsNet module captures the precise objective features and delivers the feature map to the classifier to detect the destruction region. In the experiment of region destruction detection in remote sensing images after the 2010 earthquake in Jacmel, Haiti, MA-CapsNet model achieved 99.64% accuracy on region destruction detection, which is better than the most advanced ResNet, GoogLeNet and Vision Transformer (VIT) models as well as the ablation experimental network model. The method improves the characterization capability of the model and solves the problem of poor detection accuracy of remote sensing image destruction regions in complicated backgrounds, which is of theoretical instruction for rapid acquisition of remote sensing image destruction and destruction evaluation.