This study presents a performance evaluation framework for university teachers based on the adaptive neural fuzzy inference system (ANFIS), aiming to enhance teaching quality and institutional management through a scientific, objective, and comprehensive assessment mechanism. The proposed method begins by developing a robust evaluation index system that integrates key dimensions of academic activity, including teaching performance, research contributions, and fundamental faculty information. A total of 16 sub-indicators are incorporated into the evaluation framework. To optimize data processing and reduce redundancy, factor analysis is applied, simplifying the indicator set while maintaining the integrity and effectiveness of the evaluation process. The core of the system leverages the strengths of both fuzzy logic and neural networks, combining the capacity of fuzzy systems to handle imprecise and uncertain information with the adaptive learning capabilities of neural networks. This hybrid approach improves the accuracy, interpretability, and adaptability of the evaluation results. By continuously optimizing the model using training data, the system dynamically refines its rule base and parameters, eliminating the reliance on manually defined parameters common in traditional fuzzy systems. The effectiveness of the ANFIS-based evaluation model is validated through empirical experiments. The results demonstrate that the proposed model outperforms conventional methods, such as backpropagation (BP) neural networks and support vector machines (SVMs), in terms of accuracy, precision, and overall performance. This research offers a novel and practical approach for evaluating university teacher performance, enabling more accurate reflection of teaching and research outcomes, and providing valuable decision-making support for academic management.
Yang et al. (Fri,) studied this question.