Research on the construction of a sustainable scientific research capability evaluation model for university teachers based on the T-S fuzzy neural network - Jia Wen; Pinhong Zeng (PLoS ONE, 2025)
Introduction This study aims to enhance educational quality and academic standards by proposing a model based on critical research ability indicators to objectively evaluate the sustainable scientific research capabilities of university teachers. Methods Using T-S fuzzy neural network technology, we developed an evaluation model to measure the sustainability of university teachers’ research capabilities. We collected data from 126 university teachers, using 90 samples for training and 36 for testing, to ascertain the model’s applicability and accuracy. Results The T-S fuzzy neural network showcased exceptional learning efficiency and achieved a 98.15% accuracy rate in assessing the sustainable scientific research capabilities of university teachers, outperforming both Naive Bayes and BP neural networks in effectiveness. Conclusion The research successfully constructs a T-S fuzzy neural network-based evaluation model for assessing the sustainable scientific research capabilities of university teachers. With high accuracy and broad applicability, this model is an effective tool for objectively evaluating university teachers’ research capabilities, clearly achieving the study’s objective.