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The paper proposes a PID controller based on BP neural network for stable control of the pipeline conveying flow rate, taking the dredging mud pump pipeline conveying experimental bench as the research object. Based on the modeling of the experimental bench using the system identification method, the BPPID is compared with the conventional PID controller and the flow rate step change and flow rate tracking experiments are carried out using the model experimental bench. The experimental results show that the BPPID control has the ability of adaptive self-learning for abrupt changes in operating conditions, which can be used as a reference for the stabilisation of the flow rate in a real dredger mud transfer pipeline .