Traditional junction temperature estimation methods cannot be adjusted according to the health status of IGBT module in real time, which leads to inaccurate junction temperature estimation when the module is degraded. Therefore, to solve the problem of junction temperature estimation error caused by module package degradation in actual conditions, this paper established a multi-data-driven IGBT junction temperature online estimation model with artificial neural network as main body. Firstly, the saturation voltage drop was determined as a thermoelectric parameter and its composition was studied. The coupling relationship between the saturation voltage drop, collector current, chip junction temperature and package degradation are analyzed. Then, to solve the problem of temperature characteristic change of saturation voltage drop caused by package degradation, a junction temperature estimation model was constructed by combining the advantages of Miller voltage temperature characteristic and the artificial neural network algorithm driven by saturation voltage drop and collector current. And the data were extracted by building an experimental platform to complete the training of the model. Finally, by comparing the estimation error with the traditional junction temperature estimation method, the new model reduces the estimation error from 20% to about 5%.