In order to accurately and efficiently analyze the needs of power customers, thereby reducing the costs of power enterprises and increasing the added value of power service products, based on Analytic Hierarchy Process(AHP), the importance of conditional attributes is calculated, a priority relationship matrix is constructed, and the weight of power customer demand is determined by combining with fuzzy relationship judgment scales. The purity of decision tree nodes is measured and the indicator analysis is conducted on discrete and continuous node variables to determine the accuracy of power customer demand weights. A correlation extraction model is established for power customer demand, and a user profile is obtained. Taking the information differentiation values as the indicators of variable differentiation ability, the correlation coefficients between different variables are calculated. By designing correlation extraction algorithms, the power customer correlation results are obtained, and a user profile is got. Taking the information differentiation values as the indicators of variable differentiation ability, the correlation coefficients between different variables are calculated. By designing correlation extraction algorithms, the power customer correlation results are obtained. Among high, intermediate and low frequencies, the Mean Absolute Percentage Error(MAPE) values of this method are 87.3%,71.9%, and 54.1%, respectively. In intermediate-frequency customer data, the MAPE of this method is increased from 62.1% to 71.9%; in low-frequency customer data, MAPE is increased from 42.2% to 54.1%. This method has a good correlation effect.