The　reconstruction　of　the　ocean＇s　3D　thermal　structure　is　essential　to　the　study　of　ocean　interior　processes　and　global　climate　change.　Satellite　remote　sensing　technology　can　collect　large-scale,　high-resolution　ocean　observation　data,　but　only　at　the　surface　layer.　Based　on　empirical　statistical　and　artificial　intelligence　models,　deep　ocean　remote　sensing　techniques　allow　us　to　retrieve　and　reconstruct　the　3D　ocean　temperature　structure　by　combining　surface　remote　sensing　observations　with　in　situ　float　observations.　This　study　proposed　a　new　deep　learning　method,　Convolutional　Long　Short-Term　Memory　(ConvLSTM)　neural　networks,　which　combines　multisource　remote　sensing　observations　and　Argo　gridded　data　to　reconstruct　and　produce　a　new　long-time-series　global　ocean　subsurface　temperature　(ST)　dataset　for　the　upper　2000　m　from　1993　to　2020,　which　is　named　the　Deep　Ocean　Remote　Sensing　(DORS)　product.　The　data-driven　ConvLSTM　model　can　learn　the　spatiotemporal　features　of　ocean　observation　data,　significantly　improves　the　model＇s　robustness　and　generalization　ability,　and　outperforms　the　LighGBM　model　for　the　data　reconstruction.　The　validation　results　show　our　DORS　dataset　has　high　accuracy　with　an　average　R-2　and　RMSE　of　0.99/0.34　degrees　C　compared　to　the　Argo　gridded　dataset,　and　the　average　R-2　and　NRMSE　validated　by　the　EN4-Profile　dataset　over　the　time　series　are　0.94/0.05　degrees　C.　Furthermore,　the　ST　structure　between　DORS　and　Argo　has　good　consistency　in　the　3D　spatial　morphology　and　distribution　pattern,　indicating　that　the　DORS　dataset　has　high　quality　and　strong　reliability,　and　well　fills　the　pre-Argo　data　gaps.　We　effectively　track　the　global　ocean　warming　in　the　upper　2000　m　from　1993　to　2020　based　on　the　DORS　dataset,　and　we　further　examine　and　understand　the　spatial　patterns,　evolution　trends,　and　vertical　characteristics　of　global　ST　changes.　From　1993　to　2020,　the　average　global　ocean　temperature　warming　trend　is　0.063　degrees　C/decade　for　the　upper　2000　m.　The　3D　temperature　trends　revealed　significant　spatial　heterogeneity　across　different　ocean　basins.　Since　2005,　the　warming　signal　has　become　more　significant　in　the　subsurface　and　deeper　ocean.　From　a　remote　sensing　standpoint,　the　DORS　product　can　provide　new　and　robust　data　support　for　ocean　interior　process　and　climate　change　studies.