Article: Construction and application analysis of English translation teaching model based on multi-strategy bee colony algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.41 pp.1 - 20 Abstract: This paper constructs an intelligent English translation teaching model based on a multi-strategy bee colony algorithm, capable of realising personalised teaching by dynamically adjusting content and learning paths. Experimental data indicate that the model significantly enhances student performance; average scores in large classes increased by over 13 points (approximately 19.6%), demonstrating strong scalability. Compared to traditional algorithms like PSO and GAE, the model achieves stability within just 18 iterations, significantly optimising error rates compared to previous fluctuations. Furthermore, it drastically reduces task completion time - handling 60-word tasks in under one hour, whereas traditional neural models require over nine. While senior students exhibit rapid short-term gains and juniors show stable long-term improvement, the model ultimately validates itself as a highly efficient, precise, and personalised solution for modernising translation teaching. Inderscience Publishers - linking academia, business and industry through research

Title: Construction and application analysis of English translation teaching model based on multi-strategy bee colony algorithm

Authors: Yifan Wang; Hai Jin

Addresses: Wuhu Vocational Technical University, Wuhu, 241003, China ' Wuhu Vocational Technical University, Wuhu, 241003, China

Abstract: This paper constructs an intelligent English translation teaching model based on a multi-strategy bee colony algorithm, capable of realising personalised teaching by dynamically adjusting content and learning paths. Experimental data indicate that the model significantly enhances student performance; average scores in large classes increased by over 13 points (approximately 19.6%), demonstrating strong scalability. Compared to traditional algorithms like PSO and GAE, the model achieves stability within just 18 iterations, significantly optimising error rates compared to previous fluctuations. Furthermore, it drastically reduces task completion time - handling 60-word tasks in under one hour, whereas traditional neural models require over nine. While senior students exhibit rapid short-term gains and juniors show stable long-term improvement, the model ultimately validates itself as a highly efficient, precise, and personalised solution for modernising translation teaching.

Keywords: multi-strategy swarm algorithm; intelligent translation teaching; personalised learning paths; teaching optimisation model; improvement of translation ability.

DOI: 10.1504/IJICT.2026.153378

International Journal of Information and Communication Technology, 2026 Vol.27 No.41, pp.1 - 20

Received: 02 Sep 2025
Accepted: 24 Nov 2025
Published online: 06 May 2026 *

Title: Construction and application analysis of English translation teaching model based on multi-strategy bee colony algorithm

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