Abstract: The main problem of Chinese text classification is the high dimenmonat teature space particle swarm optimization, KNN text classification algorithm is proposed. It uses chaotic particle swarm algorithm to traverse feature space of the training set, selects the feature subspace, and then it uses KNN algorithm to classify text in feature subspace. In particle swarm' s iterative process, It uses chaotic map to guide swarms for chaotic search,it makes the algorithm out of local optimum, and expands the ability of finding global optimal solution. Experimental results show that the proposed new classification algorithm for Chinese text classification is effective, the classification accuracy and recall are better than KNN algorithm.