In view of that the traditional genetic selection method selects a large number of redundant genes, which leads to a lower sample fore cast accu racy, a feature gene selection method is put forward based on the signal noise r ation and the neighborhood rough set(SNRS). Firstly, the signal to noise ratio (SNR) i ndex is used to obtain the primary feature subset which have a greater impact on classification. Secondly, the rough neighborhood intensive algorithm is used to o ptimize the primary feature subset. Finally, feature gene subset is classified b y different classifier. Experiment results show that the proposed method can get a higher classification accuracy using less feature gene than the traditiona l ones, which verifies the feasibility and validity of the method.