This paper presented an experiment on semantic role labeling by using SVM.This experiment was based on Chinese PropBank 5.0,which consisted of 1 652 sentences.The role-labeling set of this experiment included subject,object,indirect object,time and location.It used two-phase classification method with eight features,including path,phrase type,etc.For the small scaled training set,the experiment on testing set could reach the accuracy of 89.73% and the recall of 91.26% for semantic role labeling.Results highlight the effectiveness and efficiency of proposed approach for shallow semantic parsing of Chinese.