There are often missing body weight data for some individuals in the breeding procedure of freshwater snails of Bellamya．In order to utilize the information of all individuals with excellent breeding performance as much as possible,an artificial neural network was trained on four morphological traits (including shell height,shell width,aperture height and aperture width) and body weight data of 784 individuals collected from five geographical populations including Yangcheng Lake,Jiangyin,Guanlian Lake,Hong Lake and Xiantao．After this,another 261 individuals sampled from Tai Lake were used to test the artificial neural network model．In the end,anartificial neural network model for predicting missing body weights of Bellamya snails was successfully established．In addition,the artificial neural network model was used to predict the missing body weights of 201 Bellamya snails collected from Weishan Lake,and the determination coefficient of this method was compared with those of two other missing value prediction methods (i.e.,the predicted mean matching method and the random forest prediction method)．The results showed that the determinationcoefficient of the artificial neural network model constructed in this study was 0.96 for predicting the missing body weights,which was obviously higher than those of the predictive mean matching method(0.87)and the random forest prediction method(0.85)．The results obtained here could provide an efficient method for the prediction of missing values of body weight involved in the breeding process of the Bellamya snails,helping to improve the efficiency of Bellamya breeding．