Currently,litter size trait is an important indicator to measure sow fertility and play important roles in determining total income of pig farm in China. An accurate prediction of these traits in the early life of an animal will allow pig producers to adjust their management practices in order to cull bad sows early and improve the reproductive ability of core sows. However,there are many factors not only influence sow’s litter size trait,but also influence each other. Traditional prediction methods may not be powerful enough to capture complex interactions while avoiding overfitting. In this case,learning algorithms that can learn from current data to predict the animal’s future performance offers promise. In this study,firstly,the sow’s production data,including total number of piglets born (TNB),number born alive (NBA),number of healthy piglets(NHP),number of piglets aged 5 day (N5D) and number of piglets weight above 1 kg (NPWA1) were processed and described statistically. Then,the R-package Boruta was used to screen out important eigenvalues affecting the litter size traits of sows,such as breed,parity,mating season,delivery season,gestation period,interval birth and birth litter weight. Last,regression analysis was performed by traditional linear regression method and three different machine learning methods including decision tree (DT),K-nearest neighbor (KNN) and support vector machine (SVM). The evaluation index of model including R2 and MSE are obtained by ten flod cross validation. Additionally,modeling methods was assessed by these indexes and best model was screened scatter plot using a part of original data. The results showed that the R2 of all regression analysis methods in TNB,NBA,NHP,N5D NPWA1 was over 0.71 (0.71-0.88),which showed that the selection of characteristics is correct. The SVM model was not only significantly better than other machine learning methods (P<0.05),but also better than traditional regression method in predicting TNB,NBA,NHP,N5D and NPWA1. The SVM model of NPWA1 is the best in all models. Therefore,machine learning methods will become a new approach for pig producers to breed high-fecundity sows in the future.