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Modern biomedical research generates vast, multi-modal datasets (multi-omics) from the same patient cohorts, offering an unprecedented opportunity to understand complex diseases. However, integrating these heterogeneous data views to predict clinical outcomes like patient survival presents significant statistical challenges. These challenges include data heterogeneity, high dimensionality, inherent zero-inflation due to technical dropouts or biological absence, and the need to incorporate prior biological knowledge. We propose the Bayesian Multi-view Graph Convolutional Network (BMGCN), a deep generative framework designed to address these challenges. BMGCN factorizes the data into shared and view-specific latent representations, enabling both data integration and the identification of view-specific signals. It employs graph-convolutional encoders to integrate prior biological network knowledge, a zero-inflated likelihood to accurately model sparse omics data, and a spike-and-slab prior for Bayesian view selection to identify modalities most relevant to the outcome. Finally, a semi-parametric Cox proportional hazards module allows the model to handle right-censored survival data directly. We detail the full generative model, derive the variational inference objective, and outline a comprehensive validation strategy. BMGCN provides a powerful, interpretable, and flexible framework for integrative multi-omics analysis.