Spatially resolved transcriptomics was honored as the Method of the Year 2020 by Nature Methods. This approach allows biologists to precisely discern mRNA expression at the cellular level within structurally preserved tissues. Leveraging artificial intelligence in spatial transcriptomic analysis enhances the understanding of cellular-level biological interactions and offers novel insights into intricate tissues, such as tumor microenvironments. Nevertheless, numerous existing clustering algorithms employing deep learning exhibit the potential for enhancement. In this paper, we focus on graph deep learning-based spatial domain identification for spatial transcriptomics (ST) data from multiple tumors. This identification enables the recognition of cell subpopulations in distinct spatial coordinates, aiding further studies on tumor progression, such as cell-cell communication, pseudo-time trajectory inference, and single-cell deconvolution. Initially, the gene expression profiles and spatial location information were transformed into a gene feature matrix and a cell adjacency matrix. A variational graph autoencoder was then applied to extract features and reduce the dimensions of these two matrices. Following training in the constructed graph neural networks, the latent embeddings of ST data were generated and could be leveraged for spatial domain identification. Through a comparison with established methods, our approach demonstrated superior clustering accuracy. The utilization of accurately segmented spatial regions enables downstream analyses of multiple tumors, encompassing the trajectory of tumor evolution, and facilitating differential gene expression analysis across various cell types.