In　multi-view　learning,　graph-based　methods　like　Graph　Convolutional　Network　(GCN)　are　extensively　researched　due　to　effective　graph　processing　capabilities.　However,　most　GCN-based　methods　often　require　complex　preliminary　operations　such　as　sparsification,　which　may　bring　additional　computation　costs　and　training　difficulties.　Additionally,　as　the　number　of　stacking　layers　increases　in　most　GCN,　over-smoothing　problem　arises,　resulting　in　ineffective　utilization　of　GCN　capabilities.　In　this　paper,　we　propose　an　attention-based　stackable　graph　convolutional　network　that　captures　consistency　across　views　and　combines　attention　mechanism　to　exploit　the　powerful　aggregation　capability　of　GCN　to　effectively　mitigate　over-smoothing.　Specifically,　we　introduce　node　self-attention　to　establish　dynamic　connections　between　nodes　and　generate　view-specific　representations.　To　maintain　cross-view　consistency,　a　data-driven　approach　is　devised　to　assign　attention　weights　to　views,　forming　a　common　representation.　Finally,　based　on　residual　connectivity,　we　apply　an　attention　mechanism　to　the　original　projection　features　to　generate　layer-specific　complementarity,　which　compensates　for　the　information　loss　during　graph　convolution.　Comprehensive　experimental　results　demonstrate　that　the　proposed　method　outperforms　other　state-of-the-art　methods　in　multi-view　semi-supervised　tasks.　©　2024　Elsevier　Ltd