Whole slide imaging (WSI) is the main basis for cancer diagnosis and prognosis, characterized by its large size, complex spatial relationships, and diverse styles. Due to its lack of detailed annotations, traditional computational pathology methods are difficult to handle WSI tasks. To address these challenges, this paper proposes a WSI survival prediction model based on graph neural networks, BC-GraphSurv. Specifically, we use transfer learning pre-training to extract features containing spatial relationship information and construct the pathological relationship topology of WSI. Then, the two branch structures of the improved graph attention network (GAT) and graph convolution network (GCN) are used to predict the extracted features. We combine edge attributes and global perception modules in GAT, while the GCN branch is used to supplement local details, which can achieve adaptability to WSI style differences and effectively utilize topological structures to handle spatial relationships and distinguish subtle pathological environments. Experimental results on the TCGA-BRCA dataset demonstrate BC-GraphSurv's effectiveness, achieving a C-index of 0.795—a significant improvement of 0.0409 compared to current state-of-the-art survival prediction models. This underscores its robust efficacy in addressing WSI challenges in cancer diagnosis and prognosis.