A fault diagnosis method based on an improved black winged kite optimization algorithm (GBKA) optimized variational mode decomposition (VMD) and wide convolutional neural network (WDCNN) is proposed to address the issue of poor diagnostic performance caused by noise interference and other factors in gearbox fault diagnosis. Firstly, in response to the shortcomings of the black winged kite algorithm (BKA), which is prone to falling into local optima and premature convergence, genetic algorithm′s gene crossover recombination and mutation operations are introduced to improve BKA; secondly, using the improved GBKA to optimize VMD parameters, modal components are screened through correlation coefficients and the signal is reconstructed; finally, the reconstructed signal is input into the WDCNN model to achieve fault classification. The results indicate that GBKA has better optimization performance compared to BKA in the test function; under two operating conditions, the average fault classification accuracy of this method reached 99.645% and 99.978%,which is superior to other comparative methods. In addition, it was less affected by noise in the noise experiment, verifying the effectiveness and stability of the proposed model and providing a reliable solution for gearbox fault diagnosis.