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Given the rapid advancements in high-throughput technology, multi-omics data have become essential for identifying cancer subtypes and providing accurate medical treatments for patients. However, integrating multi-omics data and collecting patient information pose complex and challenging tasks. Although numerous integration techniques have emerged in recent years to address the challenges posed by heterogeneity and noise in omics data, most of these algorithms are based on unsupervised methods due to the lack of labeled data. This indicates there is still potential for enhancing the extraction of valuable information from omics data. This study introduces a novel framework, namely Deep Subspace Fusion based on Integrated Self-supervision (DSFIS), for the recognition of cancer subtypes. DSFIS is built on the autoencoder with a self-representation layer and guides the autoencoder to generate the most representative sample subspace structure by integrating self-supervision. This framework can not only create a comprehensive representation of the differences and similarities among patients but also more fully uncover the potential information from omics data. The DSFIS was compared to eight cutting-edge approaches for integrating multi-omics data. The experimental findings demonstrated that DSFIS effectively identified cancer subtypes according to the omics data. It achieved significant results superior to other algorithms in survival prognosis analysis and clinical correlation analysis, demonstrating that DSFIS has great potential in identifying cancer subtypes through multi-omics data.