Objective Gene chip technology has a revolutionary influence on clinical diagnosis，treatment，drug development and screening. To resolve the difficulty of high medical data’s feature reduction and small sample，high dimensions and great noise of gene expression profile，feature reduction is extremely necessary. The experimental results demonstrate that principal component analysis (PCA) and linear discriminant analysis (LDA) classification methods can effectively resolve the problem of classification of gene expression profile while maintaining higher classification accuracy. Methods PCA and LDA methods were used to extract the features and reduce the dimensions，then K-nearest neighbor (KNN) was used as a classifier. Results The experimental results on breast cancer and ovarian cancer datasets demonstrated that PCA and LDA classification methods could effectively extract feature information and greatly reduce the dimensions of medical data while maintaining high classification accuracy. Conclusions The application of feature reduction methods for gene expression data classification of cancer can assist clinicians to discover new disease characteristics and improve diagnosis accuracy.