According to the image deep clustering method and system based on self-supervised comparative learning, comparative learning is utilized to improve the discrimination of embedding, and under the condition of not giving human annotations, the comparative learning can learn the embedding with high cosine similarity and strong discrimination for semantically similar samples by discriminating the samples. On the basis, according to the technical scheme, the subtasks capable of simplifying the learning process are mined, and due to the fact that the intra-class difference of samples of the same class is smaller than that of samples of different classes, it is determined that the subtasks are the most natural division mode according to the classes of the samples. Therefore, compared with a mixedexpert system, highly professional experts are encouraged, each expert is good at processing samples of a specific category, and a good clustering result is naturally obtained. Meanwhile, compared with a hybrid expert system, a single objective function is optimized, clustering degradation can be prevented without processing such as pre-training or regular terms, and the method can be applied tounsupervised clustering tasks of more complex images.