The healthy condition of the drive motors bearing is an important premise to realize the reliable operation of the pure electric vehicles and avoiding safety accidents.Due to the lack of the state monitoring methods of the rolling bearing,a new method based on sparse autoencoder (SAE)and support vector machine (SVM) for rolling bearing of pure electric vehicles condition monitoring is proposed. In terms of feature extraction, the time domain, frequencydomain and timefrequencydomain feature sets of rolling bearing vibration signals are used to construct highdimensional data sets, and the data fusion with multilayer SAE is performed to eliminate feature redundancy, which obtains more robust concise features.In terms of condition monitoring,the characteristic representation of bearing conditionis input into SVM for training to obtain a bearing condition monitoring model. Finally, the effectiveness of the method is evaluated by designing a bearing of pure electric vehicle motor condition experiment.The results show that comparing with the traditional feature + SVM, the monitoring method of rolling bearings of pure electric vehicles based on SAESVM is more accurate and reliable.