Construction of a Higher Vocational Culinary Education Quality Evaluation Model for University-Industry Integration Based on Adaptive BP and DDAE-SVR

In the rapidly evolving landscape of computer science, the integration of advanced computational techniques into interdisciplinary domains has become paramount. This study addresses the critical need for robust evaluation models in vocational education, emphasizing the synergy between academic institutions and industry partnerships. Traditional assessment methods in culinary education often rely on static metrics such as student grades, attendance rates, and standardized test scores. These metrics fail to capture the dynamic and evolving nature of culinary skills, which require the integration of both practical hands-on experience and theoretical knowledge. The challenges in culinary education include continuously adapting to changing industry trends, varying student learning styles, and the demand for real-time industry-relevant skills, which traditional methods do not adequately assess. To overcome these limitations, we propose an innovative evaluation framework that leverages Adaptive Evaluation Strategy for Educational Quality (AESEQ) and An Adaptive Attribution Strategy (AQAN). This model is designed to dynamically adjust to the evolving educational landscape, effectively capturing both quantitative and qualitative indicators of educational quality. The integration of DDAE enhances the model’s ability to manage complex, high-dimensional data, while the SVR component ensures precise predictive analytics. By incorporating deep feature extraction with noise reduction mechanisms, the model achieves greater generalization capability across diverse educational scenarios. Empirical validation demonstrates that our approach significantly outperforms traditional models in accuracy and reliability, offering a comprehensive tool for stakeholders to assess and enhance educational outcomes. This research contributes to the broader discourse on digital education within computer science, showcasing the transformative potential of computational methods in educational assessment and informing future policy and practice in data-driven educational reform.