The contact resistance is an important parameter of the electrical contact performance between the reaction conductors. In practice, the empirical formula is often used to calculate the contact resistance, which is difficult to meet the requirements. To solve this problem, genetic algorithm (GA) combined with BP neural network to predict the contact resistance. Through experiment, the data are obtained, and the genetic algorithm optimized BP neural network, BP neural network and regression analysis model are respectively used for training and testing, and the errors obtained by each algorithm are compared. The results of error comparison show that genetic algorithm optimizes the convergence effect of BP neural network better than the other two algorithms, and the average relative error of the contact resistance model obtained by genetic algorithm optimization BP neural network is reduced by 4.01% compared with BP neural network, which is lower than the regression analysis 4.72%, and the forecasting effect is more stable. The contact resistance prediction model using genetic algorithm and BP neural network has better nonlinear fitting ability and higher prediction accuracy than the BP neural network prediction model alone.