In　this　article,　the　problem　of　tracking　control　for　a　class　of　nonlinear　time-varying　full　state　constrained　systems　is　investigated.　By　constructing　the　time-varying　asymmetric　barrier　Lyapunov　function　(BLF)　and　combining　it　with　the　backstepping　algorithm,　the　intelligent　controller　and　adaptive　law　are　developed.　Neural　networks　(NNs)　are　utilized　to　approximate　the　uncertain　function.　It　is　well　known　that　in　the　past　research　of　nonlinear　systems　with　state　constraints,　the　state　constraint　boundary　is　either　a　constant　or　a　time-varying　function.　In　this　article,　the　constraint　boundaries　both　related　to　state　and　time　are　investigated,　which　makes　the　design　of　control　algorithm　more　complex　and　difficult.　Furthermore,　by　employing　the　Lyapunov　stability　analysis,　it　is　proven　that　all　signals　in　the　closed-loop　system　are　bounded　and　the　time-varying　full　state　constraints　are　not　violated.　In　the　end,　the　effectiveness　of　the　control　algorithm　is　verified　by　numerical　simulation.