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In order to achieve high-precision prediction of temporal and spatial distribution of the groundwater level in shallow groundwater cones region, a model was constructed firstly based on extreme learning machine (ELM). By choosing different combination factors of groundwater recharge and discharge as the input parameters of model and observing data of 28 wells as predicted target in Shijiazhuang plain, the error of spatial distribution trend was analyzed by using ArcGIS software. The results showed that the ELM model based on the water balance theory could accurately reflect the non-linear relationship of groundwater system under the influence of human and nature activity. The root mean square error (RMSE) of model under the condition without exploitation or precipitation as input factor was two times higher than that under the condition without other factors, and the coefficient of efficiency (Ens) and coefficient of determination (R2) were further reduced. Compared with the BP model, the RMSE of ELM model was reduced by 43.6%, and the scope of error was reduced by 46.4%. Ens and R2 were improved to 0.99. The tendency of error distribution showed that it was decreased from the south and southeast to the central. The RMSE of ELM model was obviously lower than that of BP model in all the regions. The accuracy of ELM model (RMSE was less than 1.82m, Ens was higher than 0.95) was higher than that of BP model (RMSE was more than 3.00m, Ens was less than 0.85) in southern high error region. Therefore, exploitation and precipitation were the main impact factors on the groundwater dynamic in the model. Meanwhile, the stability, accuracy and space uniformity of ELM model were better than those of BP model. And the transplantation results of ELM model were more satisfactory. The model could be used to forecast groundwater level of other unknown wells based on given data. Therefore, the ELM model could be used as a recommended model for predicting groundwater level under conditions of missing hydrogeological and groundwater recharge data.