Title: A quality evaluation method for Chinese online teaching content based on deep learning

Authors: Yanling Xu

Addresses: Department of Culture and Arts, Yongcheng Vocational College, He'nan, 476600, China

Abstract: To refine the precision and efficiency of evaluating the quality of online educational content, a sophisticated Chinese online teaching content evaluation technique based on deep learning is proposed. The methodology commences with the construction of an evaluation architecture integrating a hierarchical indicator system. Subsequently, the Euclidean distance method is applied to scrutinise the correlation within Chinese online teaching materials, while feature quantification decomposition is adopted to isolate key characteristics from the content, thereby facilitating the extraction of crucial educational information. Finally, the isolated Chinese online teaching materials are utilised as inputs, with the resultant quality evaluation serving as outputs, and a deep learning-based model is developed using deep belief networks to perform a nuanced evaluation of the quality of Chinese online teaching materials. Experimental findings indicate that the method achieves a markedly reduced maximum root mean square error of just 0.28 and substantially decreases the evaluation timeframe.

Keywords: deep learning; Chinese online teaching; content of courses; quality evaluation.

DOI: 10.1504/IJCEELL.2025.150070

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.6, pp.481 - 494

Received: 17 Jul 2024
Accepted: 12 Aug 2025
Published online: 28 Nov 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article