Currently,　the　most　widely　used　site　response　analysis　methods　include　linear,　equivalent　linear　and　nonlinear　approaches.　However,　the　results　of　these　analyses　gradually　deviate　from　the　true　values　as　the　site　softens　or　the　earthquake　intensity　increases.　To　overcome　this　issue,　this　study　employs　a　1D　Convolutional　Neural　Network　model　to　simulate　the　relationships　between　the　surface　and　downhole　motions.　To　ensure　comprehensive　coverage　of　various　sites　and　earthquake　conditions,　a　careful　selection　of　stations　and　seismic　intensities　resulted　in　56　stations　and　34,538　seismic　records　from　KiK-net.　Additionally,　30　seismic　intensity　measures　and　three　site　condition　parameters　were　chosen.　The　convolutional　operation　was　utilized　to　consider　the　correlations　between　the　parameters,　enabling　the　prediction　of　surface　motion　intensity　measures.　The　performance　of　the　neural　network　model　was　assessed　based　on　residual　analysis,　and　the　model＇s　generalization　ability　was　tested　using　samples　that　were　not　included　in　the　training　dataset.　The　results　demonstrate　that　the　neural　network　can　effectively　predict　surface　motion　intensity　measures.　Furthermore,　in　comparison　with　the　equivalent　linear　and　nonlinear　methods　that　do　not　rely　on　site-specific　detailed　soil　data,　the　1D　Convolutional　Neural　Network　exhibits　superior　performance　and　maintains　high　prediction　accuracy　even　for　soft　soil　and　strong　earthquake　scenarios,　confirming　the　enhanced　predictive　capability　of　the　proposed　model.　©　2024　Elsevier　Ltd