This study addressed a critical challenge in higher education informatization, where the evaluation of teaching resource allocation and management process efficiency has traditionally relied on subjective judgment and lacked unified quantitative criteria. To overcome these limitations, a Resource Configuration and Business Coordination (RCBC) model–based evaluation framework was developed. The study utilized data from the National Higher Education Statistics published by the Ministry of Education of China and the University of California, Irvine (UCI) educational resource dataset. An evaluation index system was constructed across four dimensions: resource distribution balance, utilization efficiency, coordination effectiveness, and process bottlenecks. The collected data were cleaned, normalized, and subjected to feature extraction to ensure analytical consistency and reliability. To capture the nonlinear relationships among multidimensional indicators, a multi-objective optimization framework based on the RCBC model was established. Model parameters were determined through a hybrid weighting strategy that integrated the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM), thereby improving the robustness and rationality of parameter assignment. The effectiveness of the proposed framework was assessed through regression analysis and sensitivity testing in teaching resource allocation and process management scenarios. The results demonstrated that the optimized allocation strategy improved overall resource utilization by 14.2%, increased management process efficiency by 10.5%, and enhanced prediction accuracy by 37.3% under cross-validation conditions compared with conventional approaches. These findings indicate that the proposed RCBC model provides superior performance in evaluating and optimizing educational resource allocation and operational processes. Overall, this study offers a data-driven framework for higher education informatization and provides both theoretical support and practical guidance for the development of intelligent teaching management systems.
Gao et al. (Tue,) studied this question.