To improve the accuracy of Chinese chunking and utilize the semantic information of words,a new Chinese chunking method is proposed based on conditional random fields and semantic classes.Through the analysis of Chinese chunking task and its sequential characteristics,conditional random fields that could incorporate various types of features were applied to overcome the label bias problem.Semantic features were utilized to improve the chunking performance.Experimental results show that the algorithm achieves impressive accuracy of 92.77% in terms of the F-score.A further experiment indicates the effects of feature template selection and training data′s scales on the aspect of chunking performance.