Building an intelligent education platform is an important process in the promotion of the intelligence of education. However, the artificial intelligence model on which intelligent education platforms rely consumes a large amount of electricity and energy during its training process, therefore, it is of great significance to carry out a short-term power load prediction for building an intelligent education platform. However, the major issue is the weak correlations between some attributes and power load data when considering multiple attributes during short-term power load forecasting, and the Transformer cannot capture the temporal correlation of power load data, which leads to a lack of accuracy in power load forecasting. Therefore, a short-term power load forecasting model, SF-Transformer is proposed that is based on the SR (Székely and Rizzo) distance correlation coefficient, fusion temporal localization coding and Transformer. The SF-Transformer filters the attributes that affect the power load data by using the SR distance correlation coefficient and selects the attributes that have higher SR distance correlation coefficients with the power load data. The SF-Transformer adopts fusion time localization coding, thereby combining global time coding and local position coding, which helps the model to comprehensively obtain time and localization information regarding power load data. Experiments conducted on the dataset show that SF-Transformer has a lower RMSE (root mean square error) and MAE (mean absolute error), compared with those of other power load forecasting models over two-time durations.