Abstract: A kind of outlier detection algorithm based on the improved particle swarm optimization algorithm is proposed in the paper here.And the paper focuses on solving the problem that mining algorithms had low efficiency in high dimensional environment. New algorithm using the weight of evolutionary algorithm and moving step function improve particle swarm optimization algorithm,and new algorithm unite k -means algorithm in it.At the same time,the algorithm uses the global search of particle swarm optimization algorithm and the rapid convergence of k-means algorithm advantages.So the algorithms can avoid the premature convergence of the particle swarm optimization algorithm and reduce the amount of calculation clustering center for the k-means algorithm. Experiments show that this algorithm,in high dimensional environment,can mining outliers the support degree of outlier data,which making outlier detection efficiently,accurately and practicably.