In　this　paper,　a　fixed-time　control　strategy　based　on　neural　networks　is　proposed　for　a　space　robot　with　an　input　dead　zone.　First,　a　model-based　control　method　is　proposed　based　on　the　fixed-time　convergence　framework.　Due　to　internal　errors　and　external　environmental　disturbances,　the　inertial　parameters　of　dynamic　models　generally　exhibit　uncertainties,　and　model-based　control　methods　may　exhibit　deviations　in　trajectory　tracking.　In　order　to　counteract　the　adverse　effects　of　uncertain　inertial　parameters　on　the　system　and　ensure　the　stability　of　the　control　system,　an　adaptive　learning　control　method　based　on　neural　networks　is　further　proposed.　To　enhance　the　learning　rate　of　neural　networks　and　achieve　the　convergence　of　neural　weights　within　a　fixed　time,　a　neural　network　update　rate　combined　with　virtual　control　rate　is　proposed.　In　addition,　considering　the　issue　of　the　joint　input　dead　zone　affecting　the　precision　and　stability　of　the　space　robot,　a　novel　adaptive　law　is　proposed　in　conjunction　with　system　error　signal　feedback　to　mitigate　adverse　effects.　According　to　the　Lyapunov　stability　theory,　the　stability　of　the　closed-loop　system　is　proven,　with　the　trajectory　tracking　error　converging　to　a　small　neighborhood　around　zero.　Finally,　numerical　simulation　results　demonstrate　the　effectiveness　of　the　control　algorithm.　©　2025　by　the　authors.