政策语义驱动的广东省重点领域研发计划绩效评价方法构建OA

This study addresses critical shortcomings in the performance evaluation of Guangdong provincial key areas R&D programs,namely the strong reliance on subjective weighting,insufficient quantification of policy guidance,and the inherent difficulty in objectively mapping the relative importance of evaluation indicators.To overcome these challenges,the paper proposes a novel policy semantics-driven framework specifically designed for constructing a robust and objective performance evaluation system for these crucial programs.First,natural language processing(NLP)techniques are applied to official policy documents,such as the Administrative Measures for the Guangdong Provincial Key Areas R&D Program(Revised),to extract core keywords and analyze semantic intensity,thereby transforming policy language into quantifiable evidence of indicator importance.Second,an analytic hierarchy process(AHP)judgment matrix is constructed based on word co-occurrence frequencies and differences in verb intensity,with indicator weights calculated using the eigenvalue method and validated through consistency tests to ensure scientific reliability.Finally,the resulting first-level indicator weights,ranks from highest to lowest,are project effectiveness(0.441),project outputs(0.356),project management(0.138),and project decision-making(0.064),objectively reflecting the policy core of"outcome orientation and effectiveness priority".This approach enables a seamless transformation from qualitative policy requirements to a quantitative weighting system,providing a replicable and auditable technical pathway for the performance evaluation of provincial fiscal science and technology programs and projects.