Thick-layer　rubber　bearings　exhibit　a　more　pronounced　steel　plate　constraint　effect　due　to　the　increased　rubber　layer　thickness.　However,　current　standards　only　specify　the　steel　plate　thickness　range　for　ordinary　rubber　bearings,　without　providing　dedicated　guidelines　for　thick-layer　rubber　bearings.　Through　experimental　research　and　simulation　analysis,　this　study　investigates　the　impact　of　steel　plate　thickness　on　the　mechanical　performance　of　thick-layer　rubber　bearings,　refines　the　relevant　theoretical　formulas,　and　develops　a　predictive　model　based　on　the　SSA-BP　neural　network.　A　thick-layer　rubber　bearing　with　a　first　shape　factor　of　2.84　was　designed,　and　vertical　compression　and　horizontal　shear　tests　were　conducted,　complemented　by　refined　numerical　simulations.　The　results　reveal　that　steel　plate　thickness　significantly　affects　vertical　stiffness　but　has　a　minimal　impact　on　horizontal　stiffness.　When　the　steel　plate　thickness　is　6　mm　(thickness　ratio　0.4),　the　vertical　stiffness　reaches　over　80%　of　its　maximum　value,　while　increasing　the　thickness　to　14　mm　(thickness　ratio　0.93)　reduces　the　change　in　vertical　stiffness　to　within　5%.　Based　on　upper　and　lower　bound　analyses　of　ultimate　stress　and　yield　plasticity　ratios,　the　study　recommends　that　the　steel　plate　thickness　should　be　1.3　to　1.6　times　the　rubber　thickness,　which　is　a　key　parameter　for　optimizing　the　performance　of　thick-layer　rubber　bearings.　The　developed　SSA-BP　neural　network　prediction　model　for　the　vertical　stiffness　of　thick-layer　rubber　bearings　demonstrates　excellent　generalization　capability　and　high　predictive　accuracy.