The　present　study　investigates　the　intermittent　sampled-data　synchronization　for　a　class　of　space-varying　reaction-diffusion　neural　networks.　In　the　proposed　synchronization　strategy,　the　intermittent　sampled-data　controller　is　activated　only　during　the　work　time.　We　introduce　a　switching　Lyapunov　functional　respectively　related　to　the　work　and　rest　intervals,　which　is　segmented,　time-dependent,　and　continuous　in　time.　By　using　Lyapunov　stability　theory,　synchronization　criterion　is　proposed　to　ensure　the　exponential　stability　of　the　synchronization　error　system.　Futhermore　control　gain　is　determined,　and　obtained　by　solving　a　set　of　spatial　linear　matrix　inequalities　(SLMIs).　Finally,　a　numerical　example　is　presented　to　verify　the　feasibility　of　the　control　strategy.　©　2024　IEEE.