Objective To construct a model for predicting ki-67 expression in hepatocellular carcinoma using the intratumoral and peritumoral radiomic features of contrast enhanced magnetic resonance imaging （CEMRI） in the arterial phase as well as clinical imaging features. Methods A total of 120 patients pathologically diagnosed with hepatocellular carcinoma （HCC） from January 2016 to December 2024 in No. 910 Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army were retrospectively enrolled and randomly divided into a training set （84 cases） and a test set （36 cases） in a ratio of 7∶3. ITK-SNAP software was used to delineate the global region of interest （ROI） of HCC on the arterial phase MR images. The ROIs of all patients were automatically expanded outward by 2 mm， and then the intratumoral ROI areas were eliminated to obtain the peritumoral ROI. With the help of PyRadiomics software， 1 198 intratumoral and peritumoral radiomic features were extracted. Spearman correlation analysis， maximum relevance-minimum redundancy （mRMR）， and least absolute shrinkage and selection operator （LASSO） regression were used to reduce the data dimension and select the best features. Then， a radiomics model of the logistic regression （LR） machine learning algorithm was constructed. A combined model including clinical imaging features and radiomics features was established. The area under the curve （AUC）， accuracy， sensitivity， specificity， positive predictive value （PPV）， negative predictive value （NPV）， calibration curve and decision curve analysis （DCA） were used to evaluate the efficacy of the intratumoral and peritumoral radiomics features combined with clinical imaging features model in predicting ki-67 expression in hepatocellular carcinoma. Results The intratumor model exhibited an efficacy in predicting the expression of ki-67 in hepatocellular carcinoma with AUC values of 0.817 and 0.787 in the training set and test set， respectively. The peritumoral model showed an efficacy with AUC values of 0.805 and 0.633 in the training set and test set， respectively. The intratumoral and peritumoral model demonstrated AUC values of 0.874 and 0.836 in the training set and test set， respectively. The combined model constructed by integrating the intratumoral and peritumoral model with clinical imaging features yielded AUC values of 0.877 and 0.849 in the training set and test set， respectively， indicating clinical imaging features improved the performance of the model. DCA showed that the combined models all had good clinical benefits， with the intratumoral and peritumoral model performing the best. Conclusion The intratumoral and peritumoral radiomics model based on CEMRI arterial phase combined with clinical imaging data can accurately predict the expression of ki-67 in hepatocellular carcinoma. This combined model yields the best clinical benefit.