Big Data-Driven Entropy Weight AHP Fusion Algorithm Based on 5E2S Model in Performance Evaluation of Large-Scale Equipment in Universities

In the context of the rapid development of higher education, the management of instruments and equipment in universities faces issues such as structural imbalance in resource allocation, low utilization efficiency, and insufficient openness and sharing. This study proposes a performance evaluation system based on the “5E2S” model, which integrates qualitative and quantitative methods to construct a classified evaluation system, dynamic weight adjustment, and a new “end-of-life equipment dynamic evaluation” system to track the value and potential of retired assets in real-time. The study introduces a big data-driven entropy weight - AHP fusion algorithm to automatically correct the weights of evaluation indicators, deepen the university’s characteristic indicators, and improve the accuracy of evaluation. The empirical analysis verifies the effectiveness of the model, providing a systematic solution for the management of university instruments and equipment, and promoting the connotative development of higher education and the modernization of governance capabilities.