Objective: To develop a combined prediction model that integrates multi-phase contrast-enhanced imaging, intratumoral heterogeneity (ITH), time-series radiomics (TSR) and delta-radiomics (DR) features for assessing the therapeutic response of hepatocellular carcinoma (HCC) to immuno-combination therapy, and to build a clinical decision support system (CDSS) upon it. Methods: Sixty-two HCC patients who received immuno-combination therapy at the First Affiliated Hospital of Nanjing Medical University between January 2021 and December 2024 were retrospectively enrolled. Traditional radiomics, ITH, TSR, DR and clinical test index density (CTId) features were extracted from arterial, portal-venous and delayed-phase images. A three-stage feature selection pipeline was employed to identify the optimal feature set, and multiple machine-learning classifiers were trained to construct the combined model, based on which a CDSS was subsequently developed and evaluated. Results: The combined model yielded a validation AUC of 0.821 (95%CI: 0.629-0.987) for predicting disease progression, significantly outperforming single-phase radiomics models (0.706-0.738), the ITH-only model (0.752) and the clinical model (0.685) (all P<0.05). AUCs for predicting grade ≥II and ≥III complications reached 0.803 and 0.845, respectively. Radiomics-based risk stratification independently predicted both progression-free survival (HR=4.36, 95%CI: 1.94-9.80) and overall survival (HR=4.23, 95%CI: 1.78-10.08) (both P<0.001), with stable performance in the early-stage (TNM I-II) subgroup. The developed CDSS completed risk stratification within 60 s per case, showing perfect agreement with manual analysis (Kappa=1.00). Conclusion: The combined radiomics model integrating multi-phase and longitudinal dynamics accurately characterises therapeutic response and complication risk in HCC immuno-combination therapy, and the accompanying CDSS offers a quantitative decision-support tool for individualised clinical management.