The　co-electrolysis　of　CO₂　and　H₂O　through　solid　oxide　electrolysis　cells　(SOECs),　powered　by　renewable　energy　sources,　offers　a　promising　pathway　to　achieving　carbon　neutrality　in　the　chemical　industry.　However,　the　inherent　intermittency　of　renewable　energy　generation,　such　as　wind　power,　leads　to　unstable　power　input　for　electrolysis.　This　variability　induces　significant　thermal　stress　in　SOECs,　potentially　causing　cracks　or　even　system　failure.　To　address　this　challenge,　a　hybrid　deep　learning　architecture　(HDLA)　was　developed　to　control　the　temperature　gradient　of　SOECs.　The　architecture　combines　a　convolutional　neural　network　(CNN)　and　a　long　short-term　memory　(LSTM)　model　for　wind　power　prediction,　a　multi-physics　model　for　temperature　gradient　simulation,　and　a　linear　neural　network　regression　model　to　simulate　the　temperature　distribution　in　SOECs.　Training　and　verification　are　conducted　using　16　datasets　from　an　industrial　wind　farm.　The　results　demonstrate　that　the　application　of　HDLA　successfully　reduce　the　temperature　gradient　of　SOECs　from　±20°C　to　±5°C.　Additionally,　the　potential　wind　power　utilization　achieved　near-complete　wind　power　utilization,　increasing　from　18　%　to　99　%.　This　real-time　control　strategy,　which　optimizes　flow　regulation,　effectively　mitigates　thermal　stress,　thereby　extending　the　lifespan　of　SOECs　and　ensuring　continuous　carbon　reduction,　efficient　conversion,　and　utilization.　©　2025