Title: Research on centralised matching method of teaching knowledge categories based on intelligent language recognition

Authors: Lei Liu

Addresses: Chong Qing Industry Polytechnic College, Yubei, Chongqing, 401120, China

Abstract: In order to improve the accuracy and efficiency of teaching knowledge classification matching, a method of teaching knowledge classification matching based on intelligent language recognition is proposed. The deep learning network was constructed, the language files were pre-processed, and the extracted language feature vectors were used to train the network and optimise the deep learning network. The intelligent language recognition network model is established and the k-means clustering algorithm is used to acquire the model. Classification of teaching knowledge is processed centrally and the classification system of teaching knowledge is obtained. Matching problem of teaching knowledge classification system is modelled, and the corresponding undirected graph is constructed, which is converted into the matching problem with the greatest weight, and the optimal matching scheme under the category of teaching knowledge concentration is obtained. Experimental results show that the matching results based on intelligent language recognition are more accurate and more efficient.

Keywords: intelligent language recognition; ILR; knowledge category; category set; category matching.

DOI: 10.1504/IJCEELL.2021.114361

International Journal of Continuing Engineering Education and Life-Long Learning, 2021 Vol.31 No.2, pp.202 - 217

Received: 03 Jul 2019
Accepted: 29 Sep 2019
Published online: 20 Apr 2021 *

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