Objective To explore the value of radiomics with principal component analysis (PCA) based on magnetic resonance imaging (MRI) high-resolution T2-weighted images for predicting treatment response after neoadjuvant chemoradiotherapy (nCRT) of rectal cancer. Methods Clinical data of 80 patients with locally advanced rectal cancer, who received nCRT before radical resection from Jan. 1, 2018 to Dec. 31, 2018 in our hospital, were retrospectively analyzed. There were 60 males and 20 females with a mean age of (56.2±9.9) years (range, 28-74 years). All the patients underwent 3.0 T MRI examination before nCRT. The radiomics features were extracted from the high-resolution T2-weighted images. PCA was applied to reduce the dimension. The logistic regression classifier model was built using the dimension-reduced feature and pathologic complete response label. The samples were randomly divided into training set and testing set for machine learning. ROC curves were drawn and AUC, sensitivity, specificity and accuracy were calculated. Results A total of 1 409 radiomics features were extracted from MRI high-resolution T2-weighted images. Five optimal features, which could best represent the overall radiomics feature matrix, were recombined and selected by PCA method. They represented the information on 9.926 016 67×10^-1, 4.854 545 00×10^-3, 2.509 013 91×10^-3, 2.489 032 30×10^-5 and 7.372 984 50×10^-6, respectively. The mean AUC of the logistic regression classifier model was 0.761 (95% CI 0.694-0.828), sensitivity was 90.3%, specificity was 40.0%, and accuracy was 79.0%. Conclusion Radiomics with PCA based on MRI high-resolution T2-weighted images has good predictive value of treatment response after nCRT of rectal cancer.