<p>Multi-view self-supervised learning leverages data from multiple independent perspectives to learn features through self-supervised methods. However, this approach faces challenges, such as effectively integrating features from different views and maximizing the selection of negative samples. Meanwhile, histopathological images contain rich and detailed information crucial for disease diagnosis. However, the histopathologica images also encounter several challenges, including high labeling costs, data imbalance, and sensitivity to color variations. To address these issues, we propose a three-view momentum encoder model (View3M) based on multi-view self-supervised learning for histopathological image analysis. First, we employ data augmentation techniques to address the color sensitivity issue in histopathological images. Second, we construct three views to learn image features, and the cosine annealing algorithm is introduced to dynamically update the momentum encoder to maximize the number of negative samples. Next, we design a tri-view consistency contrastive loss function to compare features from different perspectives. Finally, we utilize transfer learning to tackle the challenge of limited sample sizes. Experiments conducted on three publicly available histopathological image datasets, spanning different categories and scales, demonstrate that the model achieves state-of-the-art accuracy and robustness in classification benchmarks.</p>