Abstract: In order to improve the forecast accuracy of soil moisture, a combined forecasting model GA_IPSO_BP-SVM for soil moisture is proposed based on genetic algorithm (GA), improved particle swarm optimization (IPSO), BP neural network and support vector machine (SVM). The model introduced GA and IPSO into the weight threshold selection of BP neural network to form a GA_IPSO_BP model, and then the GA_IPSO_BP and SVM models were trained and simulated separately. Finally, an established weighted model was used to combine the soil moisture forecast results of the GA_IPSO_BP and SVM models. Taking the farmland soil moisture data of 8 monitoring stations in Anqing city within a certain period as an example, the soil moisture was predicted in three time spans of after one day, after two days and after three days separately, and the forecast accuracy of soil moisture between the proposed GA_IPSO_BP-SVM model and the comparison models BP, GA-BP, PSO-BP, IPSO-BP, GA_IPSO_BP and SVM were verified and compared. The comparison results showed that the average value of relative error of soil water content forecast accuracy of the proposed GA_IPSO_BP-SVM model was the smallest. The GA_IPSO_BP-SVM model based on the combination of GA_IPSO_BP and SVM model improves the forecast accuracy of soil moisture, and is more suitable for short-term forecast of soil moisture. The proposed method could provide technical support for the formulation of water-saving irrigation schemes in agriculture.