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In previous studies, only analysis was conducted on the structure and internal flow field characteristics of the control valve labyrinth channel, but the optimization design of the anti-cavitation performance and flow performance of the labyrinth channel was relatively lacking. In order to meet the design requirements of the valve in practical engineering, the labyrinth type regulating valve needs to have channel anti-cavitation performance and flow performance. Therefore, a method based on multi-objective genetic algorithm (MOGA) and back propagation neural network(BPNN) was proposed to optimize the structure of the regulating valve labyrinth flow channel, and improve its anti-cavitation performance and flow performance. Firstly, based on the principle of hedging energy dissipation and the principle of multi-stage bucking, the arc-shaped hedging maze flow channel was designed, and a mathematical model for fluid dynamics simulation calculation was established. Then, the model was simulated using computational fluid dynamics(CFD) simulation software. And based on the simulated data, the BPNN surrogate model was constructed. The Sobol sensitivity analysis method was combined with the surrogate model. The influence of the parameters of the maze flow channel on the simulation results was analyzed. The structure of the flow channel was optimized by multi-objective genetic algorithm. Finally, a test platform was built to measure the blocking flow curve of the flow channel. The results show that the maximum flow rate of the maze flow channel obtained by the optimization algorithm is increased from 0.087 6 kg/s to 0.117 4 kg/s, which is increased by 34%. And the linear pressure difference is increased from 762.163 kPa to 811.280 kPa, which is increased by 6%. The actual maximum flow rate of the optimized maze flow channel is 0.115 9 kg/s, which is increased by 33%, and the linear differential pressure is 819 kPa, which is increased by 7%. The cavitation resistance and flow performance of the maze runner are improved at the same time, which proves the effectiveness of the simulation and the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]