Strong　mainshocks　are　typically　accompanied　by　numerous　aftershocks,　and　the　investigation　of　the　structural　failure　mechanisms　under　the　mainshock-aftershock　sequence　becomes　particularly　crucial.　However,　the　number　of　recorded　mainshock-aftershock　sequences　is　limited.　Therefore,　the　purpose　of　this　article　is　to　provide　a　reasonable　method　for　directly　generating　the　aftershock　time　histories　from　mainshock　time　histories.　Using　convolutional　network　as　the　basic　network　layer　and　conditional　generative　adversarial　network　as　the　structure,　two　models,　one-dimensional　convolution　(1D-C-DCGAN)　and　two-dimensional　convolution　(2D-C-DCGAN)　are　established　respectively　by　utilizing　the　deep　convolutional　generative　adversarial　network　to　learn　the　relationship　between　the　mainshock-aftershock　time　histories.　Then,　they　are　trained　with　972　pairs　of　the　selected　mainshock-aftershock　time　histories,　and　prediction　results　are　discussed　in　comparison.　The　results　show　that　the　two　models　are　proficient　in　generating　AS　acceleration　time　histories　that　are　closely　related　to　the　sample　trend,　in　which　the　2D-C-DCGAN　model　performing　better　in　overall　waveform　prediction,　but　with　local　spikes.　In　the　comparison　of　intensity　measures　and　response　spectra,　by　examining　coefficients　such　as　R2,　RMSE,　MAPE,　the　two　models　outperformed　the　mainstream　model　(ASK14)　on　the　dataset,　and　the　2D-C-DCGAN　model　is　more　accurate　than　the　1D-C-DCGAN　model.　The　distributions　of　intensity　measure　predicted　by　2D-C-DCGAN　model　are　closer　to　the　measured　intensity　measures,　and　its　predicted　response　spectra　are　smoother　and　better　matched　to　the　measured　response　spectra.　This　advantage　can　be　attributed　to　the　effectiveness　of　convolution　operations　on　two-dimensional　data,　allowing　the　convolutional　capabilities　to　be　fully　utilized.　©　2024　Elsevier　Ltd