The classification algorithm based on SVM (support vector machine) attracts more attention from researchers due to its perfect theoretical properties and good empirical results. Compared with other classification algorithms, structural risk minimizations based SVM achieve high generalization performance with small number of samples. The text chunking, as a preprocessing step for parsing, is to divide text into syntactically related non-overlapping groups of words (chunks), reducing the complexity of the full parsing. In this paper, we treat Chinese text chunking as a classification problem, and apply SVM to solve it. The chunking experiments were carried out on the HIT Chinese Treebank corpus. Experimental results show that it is an effective approach, achieving an F score of 88.67%, especially for a small number of Chinese labeled samples.