Aiming at the problems of abnormal life shortening and energy consumption increasing of smart meters after large-scale use, this paper presents an energy consumption and life optimization scheme of smart meters based on edge computing. The edge server is used to receive and upload the data of smart meters, and the influence factors of energy consumption and life are extracted by convolutional neural network (CNN) at the edge end, and K-means clustering algorithm is used to predict the change of power consumption, so as to obtain the energy consumption and life optimization model. The simulation results show that the service life of 1000 smart meters is increased by 73%, and the total energy consumption is reduced by 26% in the energy consumption and life optimization environment based on edge computing, which provides a research method for long-term stable operation of smart meters.