3-D　ocean　temperature　and　salinity　data　are　the　basis　for　studying　ocean　dynamic　processes　and　warming.　Satellite　remote　sensing　observations　on　the　ocean　surface　are　abundant　and　full-coverage,　while　in　situ　observations　in　the　ocean　interior　are　very　sparse　and　unevenly　distributed.　Currently,　the　remote　sensing　inversion　models　of　temperature　and　salinity　in　the　ocean　interior　are　unable　to　learn　both　global　and　local　detail　information,　and　modeling　layer-by-layer　blocks　the　connection　between　vertical　depth　levels,　resulting　in　poor　accuracy.　In　this　study,　we　proposed　a　novel　clustering-guided　and　knowledge-distillation　network　(CGKDN)　model　based　on　the　ocean　knowledge-driven　model.　The　model　introduced　K-means　clustering　for　the　partitions　of　ocean　processes,　knowledge　distillation　(KD)　fusing　global　and　local　detail　information,　and　adaptive　depth　gradient　loss　linking　the　vertical　depth　dimension,　which　enhanced　the　interpretability　and　accuracy　of　the　model.　Comparison　of　the　reconstructions　with　the　existing　major　publicly　available　datasets　through　the　validation　of　10%　EN4　in　situ　profile　observations　from　2001　to　2020　reveals　that　the　reconstructions　are　more　accurate.　Concretely,　the　average　root　mean　square　error　(RMSE)　(degrees　C)　across　time-series　and　vertical　levels　of　CGKDN/Institute　of　Atmospheric　Physics　(IAP)/OCEAN5　ocean　analysis-reanalysis　(ORAS5)/deep　ocean　remote　sensing　(DORS)　ocean　subsurface　temperature　(OST)　is　0.590/0.598/0.690/0.723,　and　the　average　RMSE　(PSU)　of　CGKDN/IAP/ORAS5　ocean　subsurface　salinity　(OSS)　is　0.101/0.103/0.106,　respectively.　Furthermore,　the　downscaled　quarter-degree　reconstructions　present　more　mesoscale　detail　signals,　consistent　with　the　ARMOR3D　data.　This　study　not　only　improves　the　estimation　accuracy　of　subsurface　temperature　and　salinity　but　also　serves　the　study　of　ocean　interior　dynamic　processes　and　variabilities　and　provides　valuable　references　for　reconstructing　other　ocean　subsurface　physical　variables.