Abstract: Aiming at the problems of the weak generalization ability and low prediction accuracy of the traditional single model, this paper proposes a combined prediction model of GM(1,1) grey model and MEA-BP neural network. The model solves the problems of the influence of large random fluctuation and low prediction accuracy of the GM(1,1) prediction model on energy consumption prediction caused by time factors. With the advantages of MEA-BP neural network parallel computation, strong fault-tolerant force and distributed information storage, it also reduces the situation where the accuracy of the prediction results is affected by data fluctuations, and solves the problem of error feedback adjustment. The total amount of national electric energy consumption from 1985 to 2020 is selected as the modeling data, and the prediction results of linear regression, triple exponential smoothing, GM(1,1), BP neural network, MEA-BP neural network and other models are analyzed and compared. The results show that, compared with other models, GM(1,1)-MEA-BP combination model has the highest prediction accuracy and the smallest error, with MAPE value reaching 0.006 5 and RMSE value reaching 977.996 1. It is proved by an example that GM(1,1)-MEA-BP combination model has a high prediction accuracy of the electric consumption in China, which provides a basis for national macro intelligent scheduling in energy.