Abstract:

To address the structural mismatch between talent supply and demand in local undergraduate institutions during the intelligent era, and aligned with the strategic requirements of the Outline of the Plan for Building a Leading Country in Education (2024–2035), this study aims to construct a quality evaluation system that dynamically responds to external demands. Adopting the CIPP model as the theoretical framework, the study comprehensively integrates Python crawler technology, Grounded Theory, and policy text analysis to mine multi-source data, thereby reconstructing the core competency profile required in the intelligent era. The Delphi method was employed to screen indicators, and the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) was introduced to scientifically assign weights to the system, which comprises 4 primary, 14 secondary, and 42 tertiary indicators. The results indicate that Product evaluation holds the highest weight (0.396) within the system, identifying hard technical skills and the industry-education integration mechanism as critical determinants of talent cultivation quality. The constructed evaluation system facilitates a paradigm shift from experience-based judgment to data-driven evidence, providing methodological support and quantitative tools for resolving the structural dilemma of “employment difficulty” versus “recruitment shortage,” while enhancing the quality of talent supply and regional service capabilities.

Key words: intelligent era, local undergraduate institutions, CIPP evaluation model, Python crawler, intuitionistic fuzzy analytic hierarchy process (IFAHP)