Among a variety of complicated factors that are related to sand liquefaction,8 discriminant factors have been picked out of 72 samples in the earthquake event happened in Tangshan without losing any tiny but useful information.By calculating Gini coefficient with CART algorithm,a decision tree has been undertaken to divide the features of original sample dataset.Moreover,in order to reduce overfitting risk of a single decision tree,random forest with multiple trees have been created.Meanwhile,with 10-fold cross validation,best estimators with 5 max-depth and 20 trees can perform with much more stable and reliable results.The research shows that,compared to standard penetration test from Code for seismic design of buildings,both decision tree and random forest have a better predicting precision, especially there have been no false classifications with higher stability using random forest model.