关键词: K近邻分类器, 粒子群优化算法, 相似度

Abstract: An efficient algorithm SPSOKNN is proposed to reduce the computational complexity of KNN text classification algorithm，it is based on particle swarm optimization which searches randomly within training document set.During the procedure for searching k nearest neighbors of tested sample，those document vectors that are impossible to be the k closest vectors are kicked out quickly.And removing PSO evolutionary process of particle velocity impact，thus we can more rapidly find the k closest vectors of test samples.By verifying the validity of algorithm，finding the same k nearest neighbors，classification accuracy of SPSOKNN algorithm is higher than KNN algorithm.

Key words: K Nearest Neighbor（KNN） classifier, Particle Swarm Optimization（PSO）, similarity