Abstract: As electricity serves as an economic"oscilloscope", feature extraction and intelligent parameter estimation of massive electricity consumption big data are the key steps of power economy evaluation. In this paper, a modeling method and economic related feature extraction method suitable for massive power economic big data are proposed. First, the extended panel data model is constructed according to the spatiotemporal characteristics of the binary big data of electric power economy, and the stationarity and cointegration are tested.Second, taking the power consumption as the dependent variable, the weight factors of other power economic characteristics are determined by constructing regression equation. Finally, the grey relational clustering is used to extract the features, and the weight factor is used as the criterion to select the clustering center, so as to obtain the optimal feature subset. The simulation and comparative analysis of the actual power consumption data in a province shows that the proposed method can greatly eliminate redundancy on the premise of preserving the physical meaning of feature subset, meet the needs of economic evaluation, and have a certain generality.