Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases; 2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported.