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Title: Construction and application effect analysis of engineering education knowledge graph based on graph convolutional network

Authors: Aijiang Liu; Lixia Zhao

Addresses: College of Geophysics, Chengdu University of Technology, Chengdu, 610059, China ' College of Geophysics, Chengdu University of Technology, Chengdu, 610059, China

Abstract: To address complex knowledge associations in engineering education and the lack of quantitative teaching decision support, this study proposes a knowledge graph (KG) construction method integrating graph convolutional networks. The model employs a transferred dimension embedding approach to enhance entity representation and uses a BERT-BiLSTM-CRF architecture for entity-relationship extraction. Experimental results show the model achieves a mean reciprocal rank of 92.4% in link prediction and an F1 score of 91.2% on an engineering education dataset, significantly improving entity and relation extraction accuracy. A dynamic inference mechanism based on graph attention network also improves multi-hop reasoning performance. The constructed KG effectively maps relationships between courses and graduation requirements, providing data support for teaching intervention and curriculum optimisation.

Keywords: graph convolutional networks; GCN; engineering education; KG; trans D; transformers; bidirectional long short-term memory networks.

DOI: 10.1504/IJCEELL.2026.154662

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.11, pp.1 - 22

Received: 12 Sep 2025
Accepted: 23 Jan 2026
Published online: 09 Jul 2026 *