Aiming at the problems of weak fault feature information and low recognition rate of diesel engine system, a fault recognition method of key components of diesel engine based on multi feature extraction and kernel entropy component analysis (KECA) is proposed. Firstly, the collected signal is reconstructed and denoised by ensemble empirical mode decomposition, and then the variance, kurtosis, square root amplitude, peak factor and arrangement entropy are extracted as the characteristic parameters, which are reduced by KECA. Finally, support vector machine is used for fault identification and classification, and the classification results of other dimensionality reduction methods are compared. The results show that the classification results of this paper are obviously better than the other two, and the correct rate of fault identification is 96.67%, which shows that this method can effectively diagnose the fault of key components of diesel engine system, and has a good application prospect.