Pan evaporation provides basic data for the planning and management of water resources, the design of agricultural irrigation and hydrological modeling. It is a key element in the calculation of water balance. In order to improve the accuracy of pan evaporation modeling, three empirical models and three learning machine models were used to predict the pan evaporation in Jiangxi Province, including GPR, XGBoost and CatBoost models. According to the meteorological data of 16 meteorological stations in Jiangxi, such as maximum/minimum temperature, global solar radiation, extra-terrestrial solar radiation, relative humidity, wind speed, 10 different input parameters were constructed, and four statistical indicators were adopted (R2, RMSE, MBE, MAE) to evaluate the performance of the models. The statistical results show that when meteorological data is sufficient, the CatBoost 10 model is recommended as the predictive model for pan evaporation in Jiangxi Province. The values of R2, RMSE, MBE, MAE in verification period are 0.744, 0.842 mm/d, 0.006 mm/d, 0.633 mm/d, respectively. When the input combination is the same, the accuracy of the three learning machine models is better than their corresponding empirical models. This improved the model accuracy of predicting pan evaporation in Jiangxi.