In order to improve the accuracy of gearbox fault diagnosis, LVQ neural network is used to complete the gearbox fault location and identification, and the wolf pack optimization algorithm is used to optimize the model parameters. In the process of gearbox fault diagnosis, a wolf pack optimization algorithm is introduced. The LVQ neural network weights and thresholds are used as wolves. Individuals with multiple randomly generated weights and thresholds are combined to form wolves. According to the behaviors of wolves swimming, beckoning, and siege, the positions of individual wolves in the wolves are continuously updated to obtain the head wolf with the highest global fitness, in order to achieve the optimal weight and threshold, and determine the optimal gearbox fault diagnosis model. It is proved by experiments that the gearbox fault classification based on wolf pack optimization LVQ neural network has higher classification accuracy.