<p>Self-supervised learning has gained popularity for reducing the cost of large-scale dataset labeling while improving model generalization and representation. Among self-supervised learning techniques, masked learning is a prominent approach. However, current masking methods typically use regular small block masking after data augmentation, which is not truly random and can degrade the local correlation between image chunks. This paper proposes a novel self-supervised learning method based on spatially selected shifts and irregular image masks (SSIM). The method generates irregular images by threshold binarization, randomly masks the input image, and then performs spatially selective shifting and aggregated input position information operations. This approach not only avoids fixed mask shapes but also preserves and enhances the local correlation between image chunks. We benchmark our method using the DINO model, applying irregular random masking and spatial selective shifting. Experiments on the Imagenet10 dataset show improvements in linear and <i>k</i>-NN accuracy by 7.6% and 5.7%, respectively. The results demonstrate that SSIM outperforms existing self-supervised learning methods using masks. The code of this paper has been open source. <a href="https://github.com/wangzy2024/SSIM">https://github.com/wangzy2024/SSIM</a></p>