As the terminal equipment of the power grid, the degradation of smart meters is closely related to factors such as working environment and running time. Aiming at the problem that the degradation of smart meters under complex variable conditions is difficult to predict, a smart meter basic error prediction method based on the composite core support vector machine (SVM) is proposed. First, analyze the degradation data of smart meters, and use Pearson correlation analysis to find environmental variables that are highly correlated with the basic errors of smart meters. Then, in order to further extract the data degradation features, the fuzzy C-means clustering algorithm is used to cluster the smart meter degradation data and determine the degradation feature vector. Finally, based on the Gaussian radial basis kernel function and polynomial kernel function, a new composite kernel SVM model is constructed to predict the basic error of smart meters. The performance of the composite core SVM model is verified by combining the degradation data of smart meters in Xinjiang. The experimental results show that the composite core SVM model proposed in this paper can accurately predict the basic errors of smart meters in complex environments, and its prediction accuracy is higher than that of Bayesian methods. Neural network method and classic SVM method.