Abstract: Taking the structural parameters of an intake pipe as the research object,this paper proposes an integrated optimization by combining neural and genetic algorithms to improve the power and economic performance of the engine.By building a one-dimensional performance simulation model of the engine,the boundary parameters of the model are modified in combination with the external characteristic performance curve,and the quantitative effects of the length and diameter of the intake pipe on the engine performance are studied.Further,based on the neural network genetic algorithm,the optimal parameters of the intake pipe applicable to different working conditions are analyzed.The results show that the torque increases significantly in an engine speed range of 4 000 to 5 500 r/min,while the specific fuel consumption reduces significantly in an engine speed range of 5 000 to 7 000 r/min.Under the optimization target set in this paper,the maximum torque optimization rate increases by 12.08% and the specific fuel consumption reduces by 1.51%.Based on the one-dimensional system simulation model and the neural network genetic algorithm,the structural parameters of the intake pipe are optimized.The system integration method can provide data basis for the setting of variable intake parameters.