Frontiers in Sport Research, 2024, 6(3); doi: 10.25236/FSR.2024.060308.
Yuanning Li, Minghui Song, Xiaojun Wang
Department of Physical Education, Yanshan University, Qinhuangdao, China
Security is an important factor for the success of the event. In the context of new quality productivity, science and technology will continuously improve the iteration rate of productivity, and machine learning algorithms are applied to the prediction of security scale of large-scale sports events to realize the dual goals of economic and safe events. In this study, three machine learning algorithms, namely, convolutional neural network, multilayer perceptual machine and decision tree algorithm, are used to predict the security scale of the seven Summer Olympic Games from 1996 to 2021 (variables such as security expenditure, total number of security personnel, and crime rate of the host country in the past five years). The results of the study show that the decision tree algorithm fits the true and predicted values of the security scale analysis better than the other two algorithms, and the weights of the model are more accurate within the margin of error. In the prediction and analysis of security scale of large-scale sports events, the application of decision tree algorithm can efficiently and accurately provide scientific and accurate theoretical basis for the pre-planning of security work, and also provide empirical reference.
major sporting events; machine learing algorithms; security; fitness for scale
Yuanning Li, Minghui Song, Xiaojun Wang. Research on the prediction of security fitness scale for large sports events based on machine learning algorithms. Frontiers in Sport Research (2024) Vol. 6, Issue 3: 61-69. https://doi.org/10.25236/FSR.2024.060308.
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