Although direct-current (DC) charging piles are effective power supply equipment for electric vehicles (EVs), their frequent faults pose a threat to the charging safety of EVs. Accurately predicting charging pile faults can effectively ensure the safety of EVs in the charging process. For this reason, a fault prediction model for DC charging piles based on an improved gated recurrent unit (GRU) is proposed in this study. Specifically, the common fault types of DC charging piles during charging are analyzed. Considering the small sample size of specific fault data in the actual collection, variational autoencoder (VAE)-based data augmentation is performed to expand the sample data. Then, on the basis of the current fault prediction method based on the GRU network model, this study resorts to the particle swarm optimization (PSO) algorithm to optimize GRU network parameters, employs the support vector machine (SVM) model to improve the classification function output by the network, and thereby proposes a PSO-GRU-SVM fault diagnosis model for DC charging piles. Finally, an example is discussed to compare the prediction accuracy before and after the improvement, and the confusion matrix heatmaps are comparatively analyzed. Furthermore, the proposed model is compared with two commonly used network models. The results show that the proposed method can effectively improve prediction accuracy and thus verify the feasibility of the proposed method.