Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011. The word in the feature vector of the document is grouped into the cluster in less iteration. The numbers of iterations required to obtain cluster centers are reduced by transforming clusters center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit change is used for dimension reduction. Experimental results show that, this method improve the performance by significantly reducing the number of iterations required to obtain the cluster center. The same is being verified with three benchmark datasets.