Nowadays, accurate forecasting of electricity demand has become one of the most important technologies in the development and application of big data in smart power distribution and consumption system. Be different from traditional forecasting methods based on the classification of eclectic power industry, a new monthly electricity demand forecasting method which can be applied on today's typical big data platform is presented mainly through recognizing and utilizing the multi-dimension features of big data produced in the electricity consumption. Indexes to evaluate and classify multi-dimension features of electricity consumption are established through mass data in electricity consumption. Typical electricity consumption patterns in the user space are identified and obtained by data mining. To avoid the adverse effects of the discrepancy in consumption patterns on the accuracy of the prediction, different time series prediction models for different consumption patterns are established. The proposed method makes for the analysis and processing of big data platform, and the results show that the method can significantly improve the prediction accuracy and stability compared to previous method.