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Aiming at the weakness and mutual interference of the early failure signals of rotating machinery, it is easy to cause the intelligent fault classification with low accuracy. An early fault classification method of rotating machinery based on PSO-RVMD (Particle Swarm Optimization-Related Variational Mode Decomposition) and SAE (Stacked AutoEncoder) is proposed. The main methods of intelligent classification are two phases of signal enhancement and intelligent classification. Firstly, Improved PSO-RVMD motor breakdown. - Early fault vibration signals of the bearing system, the correlation between each component signal (IMF component) and the original signal is calculated through the definition of the correlation energy ratio concept, the high correlation component is screened and reconstructed, the redundant and irrelevant Interference and noise components, to achieve signal enhancement. Finally, the enhanced early weak signal is input into the SAE model for training. The SAE model is used to extract the high-level, abstract and class-specific depth features, and the BP layer is added on the last layer. The extracted deep features are directly simulated for fault classification with the motor. The bearing system vibration signal verifies the effectiveness of this method. The method can quickly identify and diagnose the early weak faults of rotating machinery, and improve the learning and automatic classification of fault features.