<p>Recent advances in spatial transcriptomics (ST) have enabled the extraction of gene expression patterns while retaining spatial context. Identifying spatial domains is crucial for ST research. However, most existing spatial domain recognition methods cannot capture more complex relationships between gene expression profiles and spatial information. To bridge this gap, we propose a novel self-supervised learning framework named STNMAE for identifying spatial domains using a neighbor-aware multi-view masked graph autoencoder. Specifically, to fully exploit the dependencies between local neighbor information and globally similar expressions, we first construct multiple neighbor views with distinct similarity measures based on the gene expression profiles and spatial information. Additionally, we utilize a feature-masked encoder to extract more expressive embeddings. Then, STNMAE learns multiple view-unique embeddings through a multi-view autoencoder. Furthermore, the framework also uses regularization techniques through the latent representation prediction module to avoid overfitting and reduce the direct effect of input features. We apply STNMAE on seven ST datasets with different resolutions across distinct platforms. Finally, extensive evaluation confirms STNMAE’s superiority over current state-of-the-art methods, indicating a substantial improvement in ST data analysis.</p> Graphical Abstract