A novel methodology is presented to enhance Chinese text chunking with the aid of transductive Hidden Markov Models (transductive HMMs),where the chunking is considered as a special tagging problem. An attempt is thus made to utilize it via a number of transformation functions to introduce as much relevant contextual information as possible in model training. These functions enable the models to make use of contextual information to a greater extent and keep away from costly changes of the original training and tagging process. Each of them results in an individual model with certain pros and cons. Through a number of experiments, the best two models are integrated into a significantly better one. The chunking experiments were carried out on the HIT Chinese Treebank corpus, of which the results show that it is an effective approach to the recognition of Chinese chunk, achieving an F score of 8238%.