In this study, random forest (RF), gradient enhanced regression analysis (GBR), support vector regression (SVR) and deep learning neural network (DNN) were used to predict the actual evapotranspiration(Evaporation, ET) of wetland ecosystems using Fluxnet2015 global flux tower observation dataset. Through comparative study, we found that the optimal combination of input features for predicting ET including shortwave radiation, net radiation, gross primary product, air temperature, soil temperature, wind speed, precipitation, longitude, latitude and time. Furthermore, the estimation accuracy of different models was compared and analyzed using independent input datasets extracted from Fluxnet2015 datasets and ERA5-land reanalysis data. The results showed that: taking Fluxnet site data as input, SVR algorithm has a relatively high accuracy, with R² up to 0.896 and minimum RPE of 31.5%. Using ERA5-Land reanalysis data as input, except GBR algorithm, the R² of the other three methods was higher than 0.820, RPE was less than 57%. In addition, the accuracies of ET estimated by data-driven algorithms were significantly higher than the ET products in the ERA5-Land reanalysis data.