The　development　of　in　situ　observations　has　significantly　improved　ocean　heat　content　(OHC)　estimation.　However,　high-resolution　OHC　data　remain　limited,　hindering　detailed　studies　on　mesoscale　oceanic　warming　variability.　This　study　used　a　deep　learning　method-Densely　Deep　Neural　Network　(DDNN)　to　reconstruct　a　high-resolution　(0.25　degrees　x　0.25　degrees)　global　OHC　dataset　for　the　upper　2000m　ocean　from　1993　to　2023,　named　the　Ocean　Projection　and　Extension　Neural　Network　0.25　degrees　(OPEN0.25　degrees)　product.　This　deep　ocean　remote　sensing　approach　integrates　multi-source　remote　sensing　data,　including　Sea　Surface　Temperature　(SST),　Absolute　Dynamic　Topography　(ADT),　and　Sea　Surface　Wind　(SSW),　alongside　spatiotemporal　coordinates　and　in　situ　observations.　The　DDNN　model　was　trained　using　Argo-based　gridded　data　and　EN4-profile　data,　initially　undergoing　pre-training　to　assimilate　large-scale　oceanic　features,　followed　by　fine-tuning　to　enhance　its　accuracy　in　capturing　mesoscale　thermal　structures.　Our　results　demonstrate　that　the　DDNN　model　achieves　high　accuracy　across　various　depths.　Particularly,　OPEN0.25　degrees　can　effectively　capture　detailed　thermal　variations　in　regions　with　complex　dynamics,　as　well　as　the　heat　transfer　processes　within　the　ocean　interior,　outperforming　traditional　methods　in　resolution.　The　research　highlights　that,　influenced　by　strong　El　Nino-Southern　Oscillation　(ENSO)　events,　OHC　in　the　upper　700m　of　the　Pacific　Ocean　potentially　far　exceeding　expectations　over　the　past　decade.　Through　this　study,　OPEN0.25　degrees　has　demonstrated　its　critical　role　in　detecting　and　monitoring　long-term　changes　in　global　OHC　at　high　resolution.