<p>Multi-view clustering aims to mine consistent and complementary information between views to achieve superior clustering performance. However, existing studies often neglect the fusion of sample attribute features and structural features, the similarity consistency of samples between views, and the comprehensive utilization of high-confidence semantic labels. To address these challenges, we propose a High-Confidence Alignment and Clustering (HCAC) for multi-view clustering. Specifically, HCAC employs autoencoders and graph neural networks to extract attribute features and structural features from different views, respectively. It performs intra-view and inter-view fusion to derive more discriminative feature representations. To ensure high-confidence alignment across views, HCAC achieves high-confidence alignment across views by employing triplet constraints, including sample-level and cluster-level contrastive learning and similarity consistency among samples. Finally, HCAC utilizes high-confidence semantic labels to assist in the distribution alignment of clustering branch for each view, ensuring high reliability of the clustering results. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering performance.</p>