Recent　advances　in　spatially　resolved　transcriptomic　technologies　have　enabled　unprecedented　opportunities　to　elucidate　tissue　architecture　and　function　in　situ.　Spatial　transcriptomics　can　provide　multimodal　and　complementary　information　simultaneously,　including　gene　expression　profiles,　spatial　locations,　and　histology　images.　However,　most　existing　methods　have　limitations　in　efficiently　utilizing　spatial　information　and　matched　high-resolution　histology　images.　To　fully　leverage　the　multi-modal　information,　we　propose　a　SPAtially　embedded　Deep　Attentional　graph　Clustering　(SpaDAC)　method　to　identify　spatial　domains　while　reconstructing　denoised　gene　expression　profiles.　This　method　can　efficiently　learn　the　low-　dimensional　embeddings　for　spatial　transcriptomics　data　by　constructing　multi-view　graph　modules　to　capture　both　spatial　location　connectives　and　morphological　connectives.　Benchmark　results　demonstrate　that　SpaDAC　outperforms　other　algorithms　on　several　recent　spatial　transcriptomics　datasets.　SpaDAC　is　a　valuable　tool　for　spatial　domain　detection,　facilitating　the　comprehension　of　tissue　architecture　and　cellular　microenvironment.　The　source　code　of　SpaDAC　is　freely　available　at　Github　(https://github.com/huoyuying/SpaDAC.　git).