There is a certain correlation among the growth parameters of pigs, and some parameters have non-linear relationship with pig weights. When using the simple linear regression model to predict the pig weight, the collinearity among independent variables and low fit goodness were found. For these problems, based on the nearest neighbor clustering algorithm for RBF neural network, a pig weight prediction RBF neural network model was constructed with the growth parameters of 52 Landrace sows. The predicted value and measured value of the pig weight were compared by linear regression test. The regression analysis showed that the goodness of fit (R2) of RBF neural network prediction model for Landrace pig weight was 0.998, while R2 of the linear regression model was only 0.891. The results indicated that the RBF neural network-based modeling method was an effective way to build the prediction model of pig weight. It eliminated the collinearity of the independent variables in linear regression analysis, and forecasted better than linear regression model. 