Urban　seismic　damage　assessment　has　recently　become　an　emerging　research　topic　due　to　the　accelerating　global　urbanization　trend,　to　which　the　seismic　responses　of　building　clusters　to　various　earthquakes　are　a　prerequisite.　Traditional　methods　for　this　task,　including　vulnerability　analysis　and　time-consuming　time　history　analysis,　may　suffer　from　accuracy　or　efficiency　problems　especially　for　nonlinear　response　calculation.　Machine　learning　methods　allow　for　rapid　and　accurate　response　prediction,　but　current　applications　still　lack　scalability　(on　the　size　of　structures　or　earthquakes)　and　the　corresponding　real　datasets.　To　tackle　this　issue,　this　paper　proposes　an　artificial　neural　network　framework　for　simultaneously　predicting　nonlinear　seismic　responses　of　all　buildings　in　a　cluster　subjected　to　multi-earthquake　inputs.　Inspired　by　the　advanced　collaborative　filtering　techniques,　the　framework　converts　the　regional　response　prediction　into　a　matrix　completion　problem,　thereby　aggregating　information　extracted　from　historical　response　records　and　physical　characteristics　to　improve　performance.　The　framework　is　used　to　assess　nonlinear　responses　of　a　real　urban　region　consisting　of　2788　buildings　subjected　to　3798　measured　ground　motions.　The　results　clearly　demonstrated　that　the　proposed　framework　achieves　orders　of　magnitude　faster　than　time　history　analysis　and　average　errors　below　3　%　on　several　response　metrics,　showing　high　computational　efficiency　and　accuracy.