A　neural　network-based　global　fast　terminal　sliding　mode　control　method　with　non-linear　differentiator　(NNFTSMC)　is　proposed　in　this　paper　to　design　the　dynamic　control　system　for　three-axis　stabilized　platform.　The　dynamic　model　of　the　three-axis　stabilized　platform　is　established　with　various　uncertainties　and　unknown　external　disturbances.　To　overcome　the　external　disturbance　and　reduce　the　output　chatter　of　the　classical　sliding　mode　control　(SMC)　system,　the　improved　global　fast　terminal　sliding　mode　control　method　using　the　nonlinear　differentiator　and　neural　network　techniques　is　proposed　and　implemented　in　the　three-axis　stabilized　platform　system.　The　global　fast　terminal　sliding　mode　controller　can　make　the　controlled　state　approach　to　the　sliding　surface　in　a　finite　time.　To　eliminate　the　system　output　chatter,　the　nonlinear　differentiator　is　employed　to　obtain　the　differentiation　of　the　signal.　The　neural　network　is　introduced　to　estimate　the　uncertainties　disturbances　to　improve　the　stability　and　the　robustness　of　the　control　system.　The　stability　and　the　robustness　of　the　proposed　control　method　are　analyzed　using　the　Lyapunov　theory.　The　performance　of　the　proposed　NNFTSMC　method　is　verified　and　compared　with　the　classical　proportion-integral-differential　(PID)　controller,　SMC　controller　and　fast　terminal　sliding　mode　controller　(FTSMC)　through　the　computer　simulation.　Results　validate　the　effectiveness　and　robustness　of　the　proposed　NNFTSMC　method　in　presence　of　uncertainties　and　unknown　external　disturbances.