A fault diagnosis method based on LMD decomposition and sparrow search optimizing algorithm(SSA) optimizing support vector machine (SVM) was proposed for motor fault diagnosis, especially for motor bearing diagnosis. The first step was to take the combination of wavelet noise reduction and LMD algorithm to process the original signal. After wavelet noise reduction, part of the interference of the original fault signal was removed, and then a series of PF components of the original signal was decomposed. Then, the correlationanalysis method was used to select the effective PF components for signal reconstruction. The PF components of the reconstructed fault signals weredecomposed by LMD again to calculate their energy entropies, which were directly displayed by the energy diagram. The energy entropy of each PF component was formed into a group of eigenvectors and input into the fault diagnosis model of the support vector machine. The sparrow search algorithm was used to select and simulate penalty parameters and kernel parameters on the classification model of motor faults by support vector machine (SVM). The most suitable parameter combination was selected to establish the SSA-SVM fault diagnosis model for simulation experiment. The simulation experiment verified that the fault diagnosis accuracy of this method was as high as 99.2%. Compared with PSO-SVM and SVM fault diagnosis model,the experiment proves that the proposed method has more suitable fault identification ability, and has good adaptability and development for motor fault diagnosis.