Aiming at the problems of on?site verification difficulties and high periodic replacement costs of electricity metering devices, a combined electric meter error assessment model is proposed, which integrates the sparrow search algorithm (SSA), support vector regression (SVR), and fading memory recursive least squares algorithm (FMRLS). Firstly, this method utilizes an improved K?Means algorithm to classify platform areas, and imports the classified samples into an SVR model optimized by the SSA for training to build a platform area line loss rate prediction model. Then, the obtained line loss rate is taken into the improved line loss model to construct an equation for solving electricity meter errors. The FMRLS algorithm is subsequently used to solve the error equation and estimate electricity meter errors. By validating the data from a sample of low?voltage platform areas in Hebei Province, this method can effectively predict the line loss rate in low?voltage platform areas and estimate the errors in electricity meters during operation. This provides technical support for accelerating the transition of the smart electricity meter maintenance strategy from regular replacement to state rotation.