The application of various types of renewable energy on the building side is becoming more and more popular. Forecasting of building electricity consumption plays an increasingly important role in the balance of energy supply and demand, stable grid operation,and peak demand response. Although many data-driven models have been widely used in energy consumption prediction, there is still a lack of short-term prediction models with high prediction accuracy and strong generalization ability. In order to solve this problem, a classification and integration energy consumption prediction method based on the characteristics of building energy consumption and combined with data mining technology is proposed. Firstly, the recursive feature elimination method is used to screen the features of the data, and the fuzzy C-means clustering algorithmisused to cluster the training set data, meanwhile, K-nearest neighbor methodis used to classify the validation set and test set data. Then, five hybrid data-driven models combined with intelligent optimization algorithms areselected as sublearners, and each type of data is predicted respectively. Finally, multiple linear regression method is used to integrate the results. The accuracy of the ensemble prediction model is better than that of single sub-model, and it has potential to predict different building types and energy use scales.