This article focuses on the critical limitations in current undergraduate major evaluation systems within higher education, notably the scarcity of systematic evaluation at the major level and the inadequacy of singular methodologies in comprehensively reflecting complex quality dimensions. To overcome these challenges, the study constructs a multi-dimensional evaluation framework encompassing teaching resources, faculty caliber, student development, and societal impact, organized hierarchically across four first-level, nine second-level, and forty third-level indicators. The core methodological innovation lies in a combined weighting approach that integrates the Analytic Hierarchy Process (AHP) for subjective expert judgment, the Entropy Weight Method (EWM) for objective data-driven variability analysis, and the Mean-Variance Method (MVM) for statistical dispersion evaluation. This approach optimally synthesizes the strengths of these individual methods, effectively mitigating subjective bias, data noise sensitivity, and discrete deviation inherent in single-method evaluations. Empirical validation using data from a science- and technology-focused university demonstrates that the combined weighting results exhibit superior consistency with external authoritative accreditations (National First-Class Undergraduate Majors and Engineering Education Accreditation), as rigorously confirmed by significant Spearman's rank correlation analysis. The research provides a scientifically rigorous and practically viable solution for higher education institutions seeking to establish robust, multi-dimensional evaluation systems, optimize major structures, and enhance the quality and effectiveness of undergraduate talent cultivation within the context of national higher education reform initiatives.
Yang et al. (Thu,) studied this question.