Aiming at the problem of low fault diagnosis accuracy of charging piles, this paper first proposes to use multi-dimensional scale analysis method to process sample data, map the original data to a lower dimensional space, and reduce the cost of model calculation. Secondly, Sin chaos mapping and dynamic adaptive weighting are integrated into the sparrow algorithm to improve its global search ability and optimization accuracy, and then the improved sparrow algorithm is used to optimize the parameters of the support vector machine model, and the optimal diagnosis model is established. Finally, the obtained model is used to diagnose the fault diagnosis of the charging pile and output the diagnosis results. The final experimental results show that the diagnostic accuracy of the fault-diagnosis model of the charging pile proposed in this paper is as high as 95.135 1%, which is significantly higher than that of some existing commonly used models. At the same time, the support vector machine model selected in this paper has better effect and higher efficiency than other classification models.