Accurate load forecasting is one of the important tasks for power workers, and power load forecasting can be generally divided into short-term forecasting and medium- and long-term forecasting depending on the forecasting period. Compared with short-term power load forecasting, medium- and long-term forecasting is little explored by cutting-edge workers. Therefore, this study proposes an XGBoost-DNN-based algorithm that can be applied to mid-term power load forecasting. The algorithm combines the tree model with the deep neural network and introduces short-term forecasting into mid-term forecasting. According to the characteristics of the tree model, the data features are processed into high-order cross features, and in combination with the original data, the deep neural network is used to learn rich feature information. Algorithm analysis with the data of the 2017 Global Energy Forecasting Competition shows that in mid-term power load forecasting, the XGBoost-DNN model proposed by this method is more accurate than DNN and LSTM.