Abstract: In order to solve the problems of incomplete consideration and inaccurate prediction in the current lightning trip prediction model of transmission line, this paper analyzes the problems existing in the lightning trip prediction of transmission line span section, introduces the advantages of BP neural network algorithm optimized by simulated annealing genetic algorithm, and carries out the lightning trip prediction of span section. This method has the advantage of simulated annealing algorithm avoiding falling into local minimum, and genetic algorithm is used to search the optimal solution in parallel. The disadvantages of low training efficiency and local convergence value of BP neural network in model prediction are solved. In this experiment, firstly, the data are normalized, the data set is selected, and the relevant parameters of the algorithm are set. The tower account data and lightning monitoring data of a power grid in Guizhou Province are used as source data for training and analysis. The algorithm is evaluated by error standard and performance standard, and verified by test samples. The comparison of experimental results shows that the proposed algorithm is more accurate in predicting lightning trip-out at span.