In　this　article,　an　adaptive　neural　learning　method　is　introduced　for　a　category　of　nonlinear　strict-feedback　systems　with　time-varying　full-state　constraints.　The　two　challenging　problems　of　state　constraints　and　learning　capability　are　investigated　and　solved　in　a　unified　framework.　To　obtain　the　learning　of　unknown　functions　and　satisfy　full-state　constraints,　three　main　steps　are　considered.　First,　an　adaptive　dynamic　surface　controller　(DSC)　based　on　barrier　Lyapunov　functions　(BLFs)　is　structured　to　implement　that　the　closed-loop　systems　signals　are　bounded　and　full-state　variables　remain　within　the　prescribed　time-varying　intervals.　Moreover,　the　radial　basis　function　neural　networks　(RBF　NNs)　are　used　to　identify　unknown　functions.　The　output　of　the　first-order　filter,　instead　of　virtual　control　derivatives,　is　used　to　simplify　the　complexity　of　the　RBF　NN　input　variables.　Second,　the　state　transformation　is　used　to　obtain　a　class　of　linear　time-varying　subsystems　with　small　perturbations　such　that　the　recurrence　of　the　RBF　NN　input　variables　and　the　partial　persistent　excitation　condition　are　actualized.　Therefore,　the　unknown　functions　can　be　accurately　approximated,　and　the　learned　knowledge　is　kept　as　constant　NN　weights.　Third,　the　obtained　constant　weights　are　borrowed　into　an　adaptive　learning　scheme　to　achieve　the　batter　control　performance.　Finally,　simulation　studies　illustrate　the　advantage　of　the　reported　adaptive　learning　method　on　higher　tracking　accuracy,　faster　convergence　rate,　and　lower　computational　expense　by　reusing　learned　knowledge.