The reliable operation or not of smart energy metering equipment affects the accuracy of grid edge measurement and electricity metering. For this reason, this paper proposes an equipment degradation trend analysis method based on parameter optimization BP neural network. Combining with the State Grid Xinjiang High Dry Heat Test Base and the basic error data of the real-time operation of the smart energy metering devices, the Spearman correlation analysis method is used to extract the main environmental stress that affects the basic error value of the smart energy metering equipment; the function fitting interpolation (FFI) is used to eliminate the influence of missing values in the original data on degradation analysis. A BP neural network-based smart energy metering equipment degradation research model is established. Finally, an improved genetic algorithm (IGA) is introduced to optimize the BP neural network parameters to realize the backward forecast and update of the degradation trend of smart energy metering equipment. Several smart energy metering devices with different types in the base were selected to conduct various kinds of experiments. The results show that the model proposed in this paper has a high predictive ability. The average root mean square error of the prediction results is 0. 012 3, and the prediction accuracy is up to 90. 2% .