Evaluation system of college english teaching quality based on fuzzy information of artificial intelligence

Abstract The current evaluation of English teaching quality in colleges and universities faces the problems of information uncertainty and fuzziness. Traditional evaluation methods cannot accurately reflect the complex teaching effects, mainly due to the diversity of data and the fuzziness of evaluation dimensions. To address this issue, this paper proposes a college English teaching quality evaluation system that combines Fuzzy C-Means (FCM) and Takagi-Sugeno Fuzzy Inference System (TS-FIS). First, the FCM algorithm is utilized to fuzzify various teaching data and convert the evaluation dimensions into fuzzy membership degrees. Then, TS-FIS is used to infer this fuzzy information and generate comprehensive scores. Finally, a deep neural network (DNN) is employed to train historical data, dynamically adjusting the evaluation results. The findings demonstrate that the system achieves an evaluation accuracy of more than 91% when dealing with uncertainties in complex teaching environments, and the score fluctuation range is controlled within 5% during the dynamic adjustment process, which proves the effectiveness of the system in improving evaluation accuracy and adaptability. The method proposed in this paper provides an effective solution to the problem of evaluating English teaching quality in colleges and universities using fuzzy information.