Cancer, as a multifactorial regulated disease, demands precise subtype classification to support individualized treatment design and enhance survival outcomes. Although existing multi-omics data analysis reveals various facets of cancer progression, traditional methods overemphasize the consensus information across omics and neglect extracting omics-specific features, leading to challenges in addressing complex intergroup interactions and feature fusion problems. To address this problem, this study proposes a deep learning model based on a variational autoencoder, called Deep Multi-view Hierarchical Attention-based Clustering Learning (DMHACL). The model integrates multiple independent variational autoencoders through a multi-view encoder and combines them with a contrastive loss function to capture omics-specific variables and latent shared representations in different omics. Then, a hierarchical attention mechanism is introduced to fuse consistent representations across omics. In addition, this study uses a self-supervised deep embedding-based clustering algorithm to enhance clustering performance. Experimental results on six public cancer genome atlas datasets demonstrate that DMHACL outperforms five other competitive methods in cancer subtype identification. Recognizing cancer subtypes by DMHACL is biologically meaningful and interpretable in breast cancer case studies.