Objective Analysis of gene expression data is one of the most important branches of bioinformatics research.Correctly classifying the samples with pathological classification is important for tumor diagnosis and treatment.Methods This paper introduces the compressive sensing algorism for the classification of gastric cancer gene expression data.The redundant dictionary is formed by using the training set，and the random matrix with Gaussian entries builds the sensing matrix with normal row vectors.In the test stage，the sensing matrix is projected onto the test vector，and the minimum 10-norm solution is computed with orthogonal l2-norm algorithm.The distance between the reconstruction vector and the train vector is employed to determine the class of the test data.Results Compared with classification methods of K-means，SVM and so on，the experimental results show compressive sensing algorism promising aspects as high accuracy and efficiency for gene expression data classification.Conclusions This method is important for clinical diagnosis and biomedical research.