The Vietnamese lexical features are discussed and essential characteristics of Vietnamese are integrated into condition random fields (CRFs) to propose a Vietnamese word segmentation method based on CRFs and ambiguity model. The segmentation corpus consisting of 25 981 Vietnamese is obtained as a training corpus of CRFs by computer marking and artificial proofreading. Vietnamese crossing ambiguity is widely distributed in the sentence. To eliminate the effects of crossing ambiguity, 5 377 ambiguity fragments are extracted from training corpus through dictionary of the forward and reverse matching algorithm. An ambiguity model is obtained by training the maximum entropy model. Then they are both incorparted into the segmentation model. The training corpus is divided into ten copies evenly for cross validation experiments. The segmentation accuracy reaches 96.55% in the experiment. Experimental results show that the method improves the segmentation accuracy rate, the recall rate and the F value of Vietnamese word obviously, compared with Vietnamese segmentation tool VnTokenizer.