In order to solve the low efficiency of traditional fault diagnosis method, the singular value decomposition and complementary ensemble empirical mode decomposition were combined to extract the characteristics of signal and the crankshaft bearing wear was predicted by optimizing the recurrent neural network with the simulated annealing algorithm based on diesel engine of infantry fighting vehicle. The complementary ensemble empirical mode decomposition was used to decompose the vibration signal, the singular value decomposition was used to extract the features, the simulated annealing algorithm was used to optimize the recurrent neural network, and the training and prediction were further conduced. Finally, the experimental analysis of the proposed algorithm shows that the prediction accuracy is 97.48%, which is more than 5% higher than that of the ordinary recurrent neural network system.