Spatial domain identification plays a critical role in elucidating tissue function and intercellular dynamics by capturing cell type diversity, gene expression heterogeneity, and cell-cell interactions within their spatial context. The functionality of complex tissues is intrinsically linked to the spatial arrangement of distinct cell types, and recent advancements in spatial transcriptomics (ST) have been pivotal in uncovering this relationship. However, current methods often struggle with accuracy and computational efficiency when processing high-dimensional, complex datasets. To overcome these limitations, we propose the application of graph attention autoencoder for spatial domain identification. The graph attention mechanism enables the model to effectively capture both local and global gene expression dependencies, thereby enhancing the precision of spatial domain delineation. The results from experiments we conducted show that our method outperforms traditional methods across multiple datasets, offering a robust approach for spatial domain identification.