The　internal　feedback　hydrostatic　rotary　table　is　a　precision　support　device,　and　its　performance　relies　heavily　on　the　oil　pad.　However,　uncertainties　in　the　manufacturing　process　are　often　overlooked　during　the　stiffness　optimization,　affecting　the　reliability　of　the　optimized　results.　Accordingly,　this　paper　aims　to　analyze　the　influence　of　structural　parameters　on　the　stiffness　performance　of　the　internal　feedback　hydrostatic　rotary　table　and　to　perform　reliability　optimization　considering　the　uncertainties.　Initially,　a　theoretical　computational　model　of　internal　feedback　hydrostatic　rotary　table,　accounting　for　the　oil　leakage　effect,　is　proposed.　The　model＇s　accuracy　is　validated　through　comparative　simulation　calculations,　and　based　on　this　model,　the　load-bearing　performance　of　the　table　is　further　analyzed.　Subsequently,　focusing　on　the　structural　characteristics　of　the　oil　pad,　a　reliability　optimization　model　that　considers　manufacturing　uncertainties　is　proposed.　To　improve　the　optimization　efficiency,　a　Levenberg-Marquardt　Backpropagation　(LM-BP)　neural　network　is　introduced　as　a　surrogate　model　for　theoretical　calculations.　The　oil　pad　is　optimized　through　a　particle　swarm　optimization　algorithm.　Ultimately,　the　optimal　structural　size　parameters　of　the　oil　pad　are　obtained,　achieving　maximal　stiffness　under　a　high　level　of　reliability.　Both　the　stiffness　performance　and　the　reliability　level　of　the　rotary　table　are　substantially　enhanced.　The　results　indicate　that　the　proposed　method　can　significantly　improve　performance　and　reliability　in　practical　applications.