Based on the traditional load forecasting theory, this paper proposes a new short-term load forecasting method based on intelligent similar day recognition and deviation correction. Firstly, the characteristic matrix of prefecture-city and correlation factors is constructed to select the most similar day of load curve through calculating matrix correlation coefficient. On this basis, the real-time meteorological deviation correction strategy which adopts the XGBoost algorithm is established to carry out the secondary deviation correction of the load curve, so as to achieve the goal of short-term load prediction. An example study shows that this strategy can effectively improve accuracy of short-term load forecasting, and also has good adaptive characteristics. Therefore, this method can be applied to the short-term power load forecasting practice. This work is supported by Science and Technology Project of State Grid Corporation of China (No. 52150016006B).