In view of the complex correlation between gene and gene in the microarray data set，a weighted K-mean gene clustering algorithm based on random forest variable importance score was proposed. First，the proposedalgorithm begins with training random forest classifier on the microarray data，using the samples as objects and thegenes as features，variable importance scores were calculated for each gene;then，a weighted K-means clusteringwere performed with genes as objects，samples as features，and variable importance score as weighted value.Experiments were carried out on Leukemia，Breast and DLBCL three datasets. The experimental results show thatthe proposed weighted K一mean clustering algorithm has an average of 17. 7 percentage points higher than theoriginal K一mean clustering algorithm with respective to the ratio of the distance between the class and the totaldistance and has better homogeneity and difference.