Retrieving surface Soil Moisture (SM) from CYGNSS has attracted great attention in recent years, yet its accuracy and efficiency should be further improved.Here, a pre-classification strategy combined with Artificial Intelligence (AI) algorithm is proposed to predict SM from CYGNSS data.This strategy can improve the accuracy of SM estimation due to the use of AI algorithm and is versatile and easy to use.The field SM data of China in 2018 are used as real ground truth values for modeling and prediction.The results show that the predicted SM is in good agreement with the referenced SM.The correlation coefficient (R) between SM retrieved from CYGNSS and ground truth data is as high as 0.8, and the mean values of Root Mean Square Error (RMSE) and unbiased root mean square error (ubRMSE) are 0.059 cm³/cm³ and 0.050 cm³/cm³, respectively.Meanwhile, the results show that the AI-based pre-classification strategy not only significantly improves the accuracy of SM estimation from CYGNSS, but is applicable to other regression and prediction fields for its good generalization and expansibility.