Transformer overload is easy to form bubbles in the oil-paper insulation system and lead to a failure. However, there is only a transformer hot-spot temperature limit of 140℃, which fails to fully consider the effect of the actual operating state on the initial temperature of bubble formation. It is difficult to fully utilize the maximum efficiency of the equipment. To solve this problem, the domestic and foreign tests of the initial temperature of bubble formation of the oil-paper insulation are synthesized and a calculation method for the initial warning temperature of transformer bubble formation applicable to actual working conditions is proposed. According to the operating parameters such as service life of the transformer, moisture content in the oil and the altitude, the initial temperature of bubble formation of the inner oil-paper insulation system of the transformer is estimated. Then, 90% of the initial temperature of bubble formation is determined to be early warning temperature to characterize the maximum load capacity that the transformer can bear. Based on Bayesian network, a short-term load prediction model is established. Then, combined with the calculation method of winding hot-spot temperature, the short-term prediction of transformer hot spot temperature is realized. A transformer short-term overload early warning method is proposed， which is confirmed to consider fully the actual operating condition of the transformers, issue warning in advance for future overload situations, guide relevant operation and maintenance departments to carry out transformer overload shutdown and load transfer work, and reduce the occurrence of transformer shutdown and insulation failure.