This　study　introduces　a　multiple-input　convolutional　neural　network　(MI-CNN)　model　for　the　seismic　damage　assessment　of　regional　buildings.　First,　ground　motion　sequences　together　with　building　attribute　data　are　adopted　as　inputs　of　the　proposed　MI-CNN　model.　Second,　the　prediction　accuracy　of　MI-CNN　model　is　discussed　comprehensively　for　different　scenarios.　The　overall　prediction　accuracy　is　79.7%,　and　the　prediction　accuracies　for　all　scenarios　are　above　77%,　indicating　a　good　prediction　performance　of　the　proposed　method.　The　computation　efficiency　of　the　proposed　method　is　340　times　faster　than　that　of　the　nonlinear　multi-degree-of-freedom　shear　model　using　time　history　analysis.　Third,　a　case　study　is　conducted　for　reinforced　concrete　(RC)　frame　buildings　in　Shenzhen　city,　and　two　seismic　scenarios　(i.e.,　M6.5　and　M7.5)　are　studied　for　the　area.　The　simulation　results　of　the　area　indicate　a　good　agreement　between　the　MI-CNN　model　and　the　benchmark　model.　The　outcomes　of　this　study　are　expected　to　provide　a　useful　reference　for　timely　emergency　response　and　disaster　relief　after　earthquakes.