This　study　presents　a　machine　learning-based　method　for　the　destructive　power　assessment　of　earthquake　to　structures.　First,　the　analysis　procedure　of　the　method　is　presented,　and　the　backpropagation　neural　network　(BPNN)　and　convolutional　neural　network　(CNN)　are　used　as　the　machine　learning　algorithms.　Second,　the　optimized　BPNN　architecture　is　obtained　by　discussing　the　influence　of　a　different　number　of　hidden　layers　and　nodes.　Third,　the　CNN　architecture　is　proposed　based　on　several　classical　deep　learning　networks.　To　build　the　machine　learning　models,　50,570　time-history　analysis　results　of　a　structural　system　subjected　to　different　ground　motions　are　used　as　training,　validation,　and　test　samples.　The　results　of　the　BPNN　indicate　that　the　features　extraction　method　based　on　the　short-time　Fourier　transform　(STFT)　can　well　reflect　the　frequency-/time-domain　characteristics　of　ground　motions.　The　results　of　the　CNN　indicate　that　the　CNN　exhibits　better　accuracy　(R-2　=　0.8737)　compared　with　that　of　the　BPNN　(R-2　=　0.6784).　Furthermore,　the　CNN　model　exhibits　remarkable　computational　efficiency,　the　prediction　of　1000　structures　based　on　the　CNN　model　takes　0.762　s,　while　507.81　s　are　required　for　the　conventional　time-history　analysis　(THA)-based　simulation.　Feature　visualization　of　different　layers　of　the　CNN　reveals　that　the　shallow　to　deep　layers　of　the　CNN　can　extract　the　high　to　low-frequency　features　of　ground　motions.　The　proposed　method　can　assist　in　the　fast　prediction　of　engineering　demand　parameters　of　large-number　structures,　which　facilitates　the　damage　or　loss　assessments　of　regional　structures　for　timely　emergency　response　and　disaster　relief　after　earthquake.