Based on the semantic knowledge base of Chinese FrameNet (CFN) self-developed by Shanxi University, automatic labeling of the semantic roles of Chinese FrameNet is turned into a sequential tagging problem at word-level by applying IOB (inside/outside/begin) strategies to the exemplified sentences in CFN corpus, and the Conditional Random Fields (CRF) model is adopted. The basic unit of tagging is word. The word, its part of speech, its relative position to the target word, the target word, and their combination are chosen as the features. Various model templates are formed through optional size windows in each feature, and the orthogonal array within statistics is employed for screening of the better template. All experiments are based on the6 692 exemplified sentences of 25 frames selected from CFN corpus. The separate model is trained for each frame on its exemplified sentences by 2-fold cross-validation, and the processing of identification and classification for the semantic roles are taken simultaneously. Finally, with the target word given in a sentence, as well as the frame name of the target word, the experimental results on all 25 frames data for the precision, the recall, and F1-measure are 74.16%, 52.70%, 61.62%, respectively.