Article: A resource allocation method for digital online teaching platform based on classification mining Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.10 pp.68 - 84 Abstract: In the process of deepening the evolution of the digital education environment, online teaching platforms often suffer from problems such as the generalisation of heterogeneous resources, dynamic demand changes and imbalanced system architecture. Therefore, a resource allocation method for digital online teaching platforms based on classification mining is proposed. Firstly, based on the improved particle swarm optimisation algorithm, the twin support vector machine is optimised for resource classification. Secondly, the matching degree is quantified by the load difference and imbalance degree of physical nodes, and the virtual machine resource demand is constrained within the physical machine capacity. Finally, by combining real-time resource consumption and node performance indicators, a configuration function and adaptation factor are constructed to achieve balanced and optimised allocation of teaching resources. The test results show that the method maintains a stable resource allocation balance of over 90%, and the resource allocation response time is always below 1.5 seconds. Inderscience Publishers - linking academia, business and industry through research

Title: A resource allocation method for digital online teaching platform based on classification mining

Authors: Weiya Xu; Xi Lin

Addresses: Academic Affairs Office, Taizhou Vocational and Technical College of Science and Technology, Taizhou, 318000, China ' School of Trade and Economics, Taizhou Vocational and Technical College, Taizhou, 318000, China

Abstract: In the process of deepening the evolution of the digital education environment, online teaching platforms often suffer from problems such as the generalisation of heterogeneous resources, dynamic demand changes and imbalanced system architecture. Therefore, a resource allocation method for digital online teaching platforms based on classification mining is proposed. Firstly, based on the improved particle swarm optimisation algorithm, the twin support vector machine is optimised for resource classification. Secondly, the matching degree is quantified by the load difference and imbalance degree of physical nodes, and the virtual machine resource demand is constrained within the physical machine capacity. Finally, by combining real-time resource consumption and node performance indicators, a configuration function and adaptation factor are constructed to achieve balanced and optimised allocation of teaching resources. The test results show that the method maintains a stable resource allocation balance of over 90%, and the resource allocation response time is always below 1.5 seconds.

Keywords: classification mining; digitisation; online teaching platform; resource allocation; twin support vector machine; TWSVM; particle swarm optimisation; PSO.

DOI: 10.1504/IJBIDM.2026.156178

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.10, pp.68 - 84

Received: 25 Feb 2026
Accepted: 09 May 2026
Published online: 07 Sep 2026 *

Title: A resource allocation method for digital online teaching platform based on classification mining

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