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The traditional ammonia desulfurization control system has a long delay time and cannot realize real-time tracking of load. Aiming at this problem, Smith's predictive compensation device is proposed to improve the stability of the control system by canceling the pure hysteresis in the system. Although this method effectively solves the problem of lag, the tuning of the PID parameters in the system still uses the trial and error method. This method is mainly adjusted by experience, which is very time consuming and has no clear judgment standard. A method for PID parameter tuning of BP (back propagation) neural network is proposed for parameter tuning of PID. BP neural network can achieve approximation of arbitrary nonlinear functions. Through neural network learning, the best combination of proportional, differential and integral coefficients is obtained to achieve the best control effect of BP_PID.