Tribological　properties　and　wear　mechanisms　of　S32750　stainless　steel/carbon　fiber-reinforced　polyetheretherketone　(CFRPEEK)　tribopair　in　seawater　under　different　salinities,　sliding　speeds　and　loads　were　experimentally　studied.　A　novel　Physics-Informed　constraint　strategy　has　been　originally　developed　and　combined　with　sparrow　search　algorithm　(SSA)　to　optimize　a　Convolutional　Neural　Network-Long　Short-Term　Memory　(CNN-LSTM)　model　for　predicting　friction　coefficient.　The　results　indicate　that　an　increase　in　seawater　salinity　and　sliding　speed　enhances　the　tribological　performance.　Under　high　load,　an　increase　in　the　PEEK　transfer　film　leads　to　a　slight　decrease　in　friction　coefficient.　The　Physics-Informed　SSA　(PISSA)　offers　enhanced　performance　through　faster　convergence,　higher　solution　quality,　and　less　computation　time.　Additionally,　CNNLSTM　model　optimized　by　PISSA　(PISSA-CNN-LSTM)　provides　more　accurate　predictions　of　friction　coefficient.