The rapid evolution of digital-intelligence courses in higher education has created a pressing need for sophisticated quality evaluation frameworks. This study constructs a comprehensive framework that integrates critical dimensions—including course design, teaching resources, faculty competence, and learning outcomes—to address this gap. Moving beyond traditional surveys and academic assessments, we leverage advanced data mining and artificial intelligence techniques. This mixed-method approach enhances the objectivity, depth, and analytical rigor of evaluations. Our findings demonstrate that intelligent analytical tools significantly improve the reliability of assessment outcomes and facilitate actionable insights for continuous course improvement. By bridging conventional evaluation models with the demands of modern digital education, this research offers a novel, technology-driven contribution to the field, emphasizing the application of evaluation results for meaningful pedagogical and curricular refinement.