In order to overcome the limitations of low indicator precision, high weight calculation error rate, and low evaluation accuracy in traditional evaluation methods, a fuzzy evaluation method of digital teaching quality of theoretical courses in application-oriented universities under AI empowerment is proposed. This method involves the use of factor analysis to screen fuzzy evaluation indicators for teaching quality, construction of a fuzzy evaluation indicator system for teaching quality under AI empowerment, and application of the random forest algorithm to calculate indicator weights. The fuzzy evaluation results of the digital teaching quality of theoretical courses in application-oriented universities are obtained by integrating indicator weights and fuzzy comprehensive evaluation methods. The experimental results show that the proposed method achieves a minimum precision of 96.17% for teaching quality evaluation index, a minimum error rate of 3.15% for evaluation index weight calculation, and a quality evaluation accuracy above 93.3%, indicating high evaluation performance.
Xie et al. (Thu,) studied this question.