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基于深度学习和集成学习的体育经济运行指数预测模型。

Sports Economic Operation Index Prediction Model Based on Deep Learning and Ensemble Learning.

机构信息

School of Physical Education and Health, East China Jiaotong University, Nanchang 330013, China.

Sangmyung University, Seoul 03016, Republic of Korea.

出版信息

Comput Intell Neurosci. 2022 Mar 28;2022:9085349. doi: 10.1155/2022/9085349. eCollection 2022.

DOI:10.1155/2022/9085349
PMID:35387248
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8979742/
Abstract

In order to construct a prediction model of sports economic operation indicators, this paper combines deep learning and ensemble learning algorithms to integrate and improve the algorithms and analyzes the principles of the LightGBM ensemble learning model and the hyperparameters of the model. Moreover, this paper obtains appropriate intelligent algorithms according to the data analysis requirements of sports economic operation. The break-even analysis method of sports event operation is to find the critical point of the program's profit and loss by analyzing the relationship between the operating cost and profit of the sports event. In addition, this paper uses deep learning and ensemble learning to comprehensively evaluate sports events, constructs a summary evaluation structure of sports items, and evaluates the model in this paper combined with experimental research. The test results verify the reliability of the model in this paper.

摘要

为构建体育经济运行指标预测模型，本文结合深度学习和集成学习算法对算法进行整合和改进，分析 LightGBM 集成学习模型的原理和模型的超参数。此外，本文根据体育经济运行数据分析的要求，得到合适的智能算法。体育赛事运营的盈亏平衡分析方法是通过分析体育赛事的运营成本和利润之间的关系，找到项目盈亏的临界点。此外，本文还利用深度学习和集成学习对体育赛事进行综合评价，构建体育项目的综合评价结构，并结合实验研究对本文模型进行评估。测试结果验证了本文模型的可靠性。

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Comput Intell Neurosci. 2023 Jul 19;2023:9842659. doi: 10.1155/2023/9842659. eCollection 2023.

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