We introduce a novel classification algorithm for gene expression data based on the Laplacian spectra of graphs.The class center is obtained by computing the average of each class in the traning set

and the Laplacian matrices of complete graphs so called normal graphs are constructed on some samples with the minimum Euclidean distance between the class center.The sum of matched points are calculated by replacing points of standard image with test samples.The test sample is divided into the biggest one of the total matched points of the class.The effectiveness of this algorithm has been verified through the leaving-one experiments using Leukemia data and Colon cancer data.