Skip to main content

Advertisement

Log in

Designing an Artificial Intelligence-based sport management system using big data

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

These days, improving students’ learning and work efficiency needs a comprehensive approach to their physical and mental well-being, which emphasizes the critical role of teachers and institutions. To optimize the responsibilities of physical teachers and coaches, efficient administration of physical education, including sports activities, and a strong quality assurance system are required. Based on the above, this paper proposes an Artificial Intelligence (AI)-based sports management system to enhance work efficiency and ease of physical education teachers and coaches. It aims to make an in-depth study on the construction of sports performance management and physical analysis system based on data mining; later, it includes the construction of sports management systems. This paper operates in such a way that it first discusses the sports performance management and physical analysis system's construction. Second, it analyses the overall framework structure of the system by presenting a detailed description of the sports test management and test items management. In addition, it analyses sports performance management and sports performance, system management and physical performance and other functional modules contained in the system along with the database design in the system based on C4.5 that is then used as the Decision Tree (DT) Classifier for data mining. Third, it uses sports performance management of DT, and the physical analysis algorithm uses the data information gained to select the split attributes. Fourth, it divides the sports performance training data set into multiple sub-datasets to construct the performance management and physical analysis system based on data mining. Finally, the suggested system is compared with existing approaches and obtains an accuracy of 97%, which is 27% high than the selected approaches. Simulation experiments show that the proposed method has high accuracy in data mining related to sports performance and can effectively improve the efficiency and quality of physical education teachers. The proposed system will be an addition to intelligent task management with high feasibility and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

Download references

Funding

No funding was provided for the completion of this study. No funds, grants, or other support was received.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Junwei Feng.

Ethics declarations

Conflict of interest

The authors have no financial or proprietary interests in any material discussed in this article. The authors declare that they have no conflict of interest.

Ethical approval

Not applicable.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, J. Designing an Artificial Intelligence-based sport management system using big data. Soft Comput 27, 16331–16352 (2023). https://doi.org/10.1007/s00500-023-09162-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09162-0

Keywords

Navigation