Title: Design and application of digital network teaching resource system for network environment

Authors: Guobin Jun

Addresses: School of Children's Welfare, Changchun Humanities and Sciences College, Changchun, 130000, China

Abstract: As the information technique is developing, resource construction has become an unavoidable practical problem in college education. The systematic integration of teaching resources has become an important breakthrough to solve this problem. Therefore, this study first extracts hidden structural features of digital network teaching resources through data pre-processing, and adds split and merge operations to K-means algorithm to extract main features. Then LSTM is used to optimise CNN to form LSCN. Finally, LSCN is combined with the improved K-means algorithm and applied to the digital network teaching resource system. The results show that the objective function value of the final solution of the improved K-means algorithm is 115. The accuracy of LSCN model in online teaching resource database can reach 94.6% at most, and the running time is 38.6s. After combining the enhanced K-means with the LSCN model, the accuracy of the integration of online courses, digital materials and other resources in the college network education system is more than 93%. It shows that the teaching resources integration method proposed by the research has good effect and efficiency, and can provide a reference method for the further informatisation of the education system.

Keywords: network environment; teaching resources; K-means; convolutional neural network; CNN; LSTM; data mining; K-means.

DOI: 10.1504/IJES.2024.143712

International Journal of Embedded Systems, 2024 Vol.17 No.1/2, pp.1 - 11

Received: 21 Nov 2023
Accepted: 07 Jan 2024
Published online: 06 Jan 2025 *

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