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In dredging operations, the composition, particle size and concentration of the material in the mud pipeline vary greatly with the underwater topography and soil quality, which may cause flow rate fluctuations and even blockage and bursting of the pipeline. Therefore, the stable control of mud flow rate is of great significance to the efficiency and safety of mud conveying. The dredging pipeline conveying system is characterized by nonlinearity, large time lag and time-varying parameters, and the traditional PID control method is not effective. Therefore, BP neural network and traditional PID control algorithm are combined and applied to the slurry flow rate control. The relationship between mud pump inverter frequency and pipe slurry flow rate is described by a controlled autoregressive CAR model with the pipeline conveying experimental platform of Hohai University, and the model is identified offline through experiments and numerical processing. On this basis, the flow rate control performance of conventional PID, single neuron PID and BP-PID are compared by simulation. It is found that the overshoot of BP-PID controller is only 3.8% and the response time is 11s for better control performance. Finally, the flow rate control method was validated by small and large increases and decreases in mud concentration in the range of ~10% to ~30% volume concentration. The results show that the flow rate control system with BP-PID control algorithm can achieve the target flow rate quickly and stably with good adaptive control performance while ensuring the safety of conveying when the concentration is calm or fluctuating drastically.