Sanjiang Plain is located in the east of Heilongjiang Province, which belongs to the humid climate area. In recent years, Sanjiang Plains natural state has changed due to several factors, such as the warming climate and human activity. Precipitation is one of the major sources of agricultural irrigation in the irrigation area. Due to the strongly stochastic characteristic of precipitation which was influenced by many factors and the lower accuracy of single forecasting model, set pair analysis was introduced which could discuss the relation between rainfall and meteorological factors. In order to improve the training speed of the radial basis function neural network, the K-means algorithm based on density parameter was applied. In this way, the sensitivity of conventional K-means algorithm to initial clustering center was also removed. A combing model based on information entropy (IE-CM) was built, which combined the radial basis function artificial neural network based on density parameter with the grey model, and the weight of each single model was calculated by using the information entropy weight method. The constructed model was applied to forecast the rainfall over the Youyi Farm in Sanjiang Plain. The case study showed that the determination coefficient, average relative error and root mean square error of IE-CM were better than those of single models, which were demonstrated to be 0.99, 10.655% and 3.03 mm, respectively. The qualification rate of the forecasted result was 83.3%, which satisfied the requirements of hydrologic prediction. In conclusion, the built combining model could provide a new method for forecasting precipitation.