Subsurface　ocean　observations　are　sparse　and　insufficient,　significantly　constraining　studies　of　ocean　processes.　Retrieving　high-resolution　subsurface　dynamic　parameters　from　remote　sensing　observations　using　specific　inversion　models　is　possible　but　challenging.　This　study　proposed　two　kinds　of　machine　learning　algorithms,　namely,　Convolutional　Neural　Network　(CNN)　and　Light　Gradient　Boosting　Machine　(LightGBM),　to　reconstruct　the　subsurface　temperature　(ST)　of　the　ocean＇s　upper　1000　m　with　a　high　resolution　of　0.25　degrees　based　on　the　satellite-based　sea　surface　parameters　combined　with　Argo　float　and　EN4　profile　data.　We　managed　to　improve　the　spatial　resolution　of　ST　from　1　degrees　to　0.25　degrees.　We　employed　two　machine　learning　algorithms　to　set　up　monotemporal　models　of　the　four　seasons　and　time-series　models　and　adopted　the　determination　coefficient　(R2)　and　Root　Mean　Squared　Error　(RMSE)　to　evaluate　the　models＇　prediction　accuracy.　The　results　show　that　LightGBM　outperformed　CNN　in　the　case　of　small　training　samples.　By　contrast,　in　the　case　of　big　training　samples,　CNN　outperformed　LightGBM.　Meanwhile,　the　ST　with　a　high　resolution　of　0.25　degrees　predicted　by　the　time-series　CNN　model　can　better　observe　mesoscale　phenomena.　This　study　provides　more　useful　and　higher-resolution　data　support　for　further　studies　on　the　warming　and　variability　of　the　ocean　interior　under　global　warming.