This work aimed to develop a noninvasive and reliable computed tomography (CT)-based imaging biomarker to predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) via radiomics analysis. In this retrospective study, a total of 177 ICC patients were enrolled from three independent hospitals. Radiomic features were extracted on CT images, then 11 feature selection algorithms and 4 classifiers were to conduct a multi-strategy radiomics modeling. Six established radiomics models were selected as stable ones by robustness-based rule. Among those models, Max-Relevance Min-Redundancy (MRMR) combined with Gradient Boosting Machine (GBM) yielded the highest areas under the receiver operating characteristics curve (AUCs) of 0.802 (95% confidence interval [CI]: 0.727-0.876) and 0.781 (95% CI: 0.655-0.907) in the training and test cohorts, respectively. To evaluate the generalization of the developed radiomics model, stratification analysis was performed regarding different centers. The MRMR-GBM-based model manifested good generalization with comparable AUCs in each hospital (p > 0.05 for paired comparison). Thus, the MRMR-GBM-based model could offer a potential imaging biomarker to assist the prediction of ER in ICC in a noninvasive manner.Clinical Relevance-The proposed radiomics model achieved satisfactory accuracy and good generalization ability in predicting ER in ICC, which might assist personalized surveillance and clinical treatment strategy making.