Graph　Convolutional　Networks　(GCNs)　have　recently　achiev-ed　impressive　performance　in　different　classification　tasks.　However,　over-smoothing　remains　a　fundamental　burden　to　achieve　deep　GCNs　for　node　classification.　This　paper　proposes　Structure-Aware　Deep　Graph　Convolutional　Networks　(SAGCN),　a　novel　model　to　overcome　this　burden.　At　its　core,　SAGCN　separates　the　initial　node　features　from　propagation　and　directly　maps　them　to　the　output　at　each　layer.　Furthermore,　SAGCN　selectively　aggregates　the　information　from　different　propagation　layers　to　generate　structure-aware　node　representations,　where　the　attention　mechanism　is　exploited　to　adaptively　balance　the　information　from　local　and　global　neighborhoods　for　each　node.　Our　experiments　verify　that　the　SAGCN　model　achieves　state-of-the-art　performance　in　various　semi-supervised　and　full-supervised　node　classification　tasks.　More　importantly,　it　outperforms　many　other　backbone　models,　by　using　half　the　number　of　layers,　or　even　fewer　layers.