Objective Different patients may have different responses to the same anticancer drug. Understanding differences in anticancer drug responses among patients is crucial for cancer precision medicine.Methods High-throughput sequencing data make it possible to construct anti-cancer drug response classification models. Based on two classic data sets, Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC), this paper proposed a classification model, mRMR-SVM, by employing Max-Relevance and Min-Redundancy (mRMR) algorithm and Support Vector Machine (SVM). Feature genes were extracted by using variance ranking and mRMR algorithm on gene expression data,and SVM was applied to predict that an anticancer drug is sensitive or resistant to a given cell line.Results The experimental results showed that the average accuracy of mRMR-SVM is 0.904 for 22 drugs in CCLE, and 0.851 for 11 drugs in GDSC, higher than traditional SVM, Random Forest, Deep Response Forest, Deep Neural Network and CDCN.Conclusion mRMR-SVM also has good generalization due to its satisfactory classification prediction on three specific tissues. In addition, mRMR-SVM could identify biomarkers closely related to the occurrence and development of cancer.