A fixed-time adaptive neural tracking control method is firstly proposed for nonlinear pure-feedback systems with quantized input saturation and output constraints. To resolve the problem of the system with non-simulation structure, the mean value theorem is introduced firstly. The Barrier Lyapunov function is used to constrain system output, and the RBF neural network is used to approximate the unknown function based on the back stepping method. According to the fixed time control theory, we design an input signal to guarantee this system to trace desired signal at fixed time under the situation of saturation input and limited output. Besides, the input signal is quantified by a lagging quantizier, which contributes to decrease the communications rate of the control signal. and the convergence speed is unrelated to the initial state in this system. Finally, Matlab simulation software is conducted to verify the effectiveness the proposed controller.