Abstract: An efficient algorithm PSOKNN is proposed to reduce the computational complexity of KNN text classification algorithm, it is based on particle swarm optimization which has random and irected global search ability to search randomly and directed within training document set. During the procedure for searching k nearest neighbors of tested sample, the particle swarm moves jumpily, and those document vectors that are impossible to be the k closest vectors are kicked out quickly. By classifying Reuters21578，the veracity of KNNPSO is the same as that of KNN, and PSOKNN reduces approximate 70% classification than KNN.

Key words: text similarity , text classification, particle swarm optimization algorithm, KNN classifier