Models for heat transfer performance and thermal resistance of heat pipe intercooler were established based on BP neural network.The heat transfer performance and thermal resistance were predicted by means of the Levenberg Marquardt training algorithm.The prediction results were in good agreement with the experimental results.For the network model of heat transfer

the maximum relative error is 8.0% and the average relative error is 3.5%.For the network model of thermal resistance

the maximum relative error is 13.1% and the average relative error is 5.1%.Thus the prediction model of the heat pipe intercooler can be used for engineering design.The structural parameters were optimized with the BP neural network model and the optimal design parameters were obtained.The study will be the foundation for the development and application of heat pipe intercooler.