Article: Big data-driven athlete selection and training optimisation system fusing graph neural network and ensemble learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.36 pp.1 - 16 Abstract: This study proposes a big data-driven optimisation system integrating graph neural networks (GNNs) and ensemble learning to overcome the limitations of traditional athlete selection and training methods with high-dimensional, multi-modal data. The system constructs a heterogeneous graph with athletes as nodes and relationships as edges. GNNs exploit complex associations among multi-dimensional features. A novel stacked ensemble framework, incorporating XGBoost and random forest, enhances model generalisation and robustness. This creates a closed loop from static evaluation to dynamic training optimisation, enabling personalised training plans based on real-time data. Experiments on a multi-source dataset of 1,000 athletes (fitness tests, performance, and physiological indicators) show the system's potential prediction accuracy reaches 94.5%. The core GNN + Stacking module achieves 90.5% accuracy on basic feature subsets - about 12% higher than traditional models - and can reduce sports injury risk by approximately 18%. Inderscience Publishers - linking academia, business and industry through research

Title: Big data-driven athlete selection and training optimisation system fusing graph neural network and ensemble learning

Authors: Xin Mu; Jie Zheng; Shunhe Huang; Sen Zheng

Addresses: Department of Physical Education, Hankou University, Wuhan, 430000, Hubei, China ' Mongolian National University of Education, Ulaanbaatar, 140210, Mongolia ' Graduate University of Mongolia, Ulaanbaatar, 140210, Mongolia ' Mongolian University of Life Sciences, Ulaanbaatar, 140210, Mongolia

Abstract: This study proposes a big data-driven optimisation system integrating graph neural networks (GNNs) and ensemble learning to overcome the limitations of traditional athlete selection and training methods with high-dimensional, multi-modal data. The system constructs a heterogeneous graph with athletes as nodes and relationships as edges. GNNs exploit complex associations among multi-dimensional features. A novel stacked ensemble framework, incorporating XGBoost and random forest, enhances model generalisation and robustness. This creates a closed loop from static evaluation to dynamic training optimisation, enabling personalised training plans based on real-time data. Experiments on a multi-source dataset of 1,000 athletes (fitness tests, performance, and physiological indicators) show the system's potential prediction accuracy reaches 94.5%. The core GNN + Stacking module achieves 90.5% accuracy on basic feature subsets - about 12% higher than traditional models - and can reduce sports injury risk by approximately 18%.

Keywords: graph neural network; GNN; ensemble learning; athlete selection; training optimisation; big data-driven.

DOI: 10.1504/IJICT.2026.153012

International Journal of Information and Communication Technology, 2026 Vol.27 No.36, pp.1 - 16

Received: 23 Oct 2025
Accepted: 21 Dec 2025
Published online: 17 Apr 2026 *

Title: Big data-driven athlete selection and training optimisation system fusing graph neural network and ensemble learning

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