Article: Study on fuzzy comprehensive evaluation of English teaching quality based on artificial neural network Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.7 pp.35 - 52 Abstract: In order to improve the coverage and accuracy of evaluation indicators, a fuzzy comprehensive evaluation method for English teaching quality based on artificial neural networks is proposed. Firstly, association rules and rough set techniques are used to collect and analyse English teaching data, and a fuzzy comprehensive evaluation index system for English teaching quality is constructed. Secondly, a multi-layer fuzzy comprehensive evaluation model is constructed, and sample data of English teaching quality is obtained by the expert scoring method. The data are used for subsequent artificial neural network learning and prediction. Finally, a fuzzy comprehensive evaluation model for English teaching quality is constructed using BP neural network. By training and learning the internal data association pattern of the network, English teaching quality evaluation is achieved. The experimental results show that the indicator coverage of the proposed method remains stable between 94.6% and 95.4%, and the highest evaluation accuracy reaches 98%. Inderscience Publishers - linking academia, business and industry through research

Title: Study on fuzzy comprehensive evaluation of English teaching quality based on artificial neural network

Authors: Lingyan Mao; Yuting Yan; Aihua Shen

Addresses: School of Languages and Cultures, Hunan Institute of Technology, Hengyang, 421002, China; School of Humanities, Universiti Sains Malaysia, Penang, 11800, Malaysia ' School of Languages and Cultures, Hunan Institute of Technology, Hengyang, 421002, China ' Alibaba Cloud Business Division, Alibaba Group, Hangzhou, 310012, China

Abstract: In order to improve the coverage and accuracy of evaluation indicators, a fuzzy comprehensive evaluation method for English teaching quality based on artificial neural networks is proposed. Firstly, association rules and rough set techniques are used to collect and analyse English teaching data, and a fuzzy comprehensive evaluation index system for English teaching quality is constructed. Secondly, a multi-layer fuzzy comprehensive evaluation model is constructed, and sample data of English teaching quality is obtained by the expert scoring method. The data are used for subsequent artificial neural network learning and prediction. Finally, a fuzzy comprehensive evaluation model for English teaching quality is constructed using BP neural network. By training and learning the internal data association pattern of the network, English teaching quality evaluation is achieved. The experimental results show that the indicator coverage of the proposed method remains stable between 94.6% and 95.4%, and the highest evaluation accuracy reaches 98%.

Keywords: artificial neural network; English teaching quality; fuzzy comprehensive evaluation; evaluation index system.

DOI: 10.1504/IJCEELL.2026.151819

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.7, pp.35 - 52

Received: 20 Jun 2025
Accepted: 07 Oct 2025
Published online: 20 Feb 2026 *

Title: Study on fuzzy comprehensive evaluation of English teaching quality based on artificial neural network

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