摘要
近年来,我国大型体育场馆的建设不断扩大,体育场馆设施投入使用后,如何降低运营风险、保证其顺利运营是亟待解决的关键问题。研究首先构建了基于文本挖掘和支持向量机结合的因素分类与识别方法,然后运用此方法对现有学者关于大型体育场馆运营风险因素的研究成果进行数据挖掘和关键因素识别。结果显示,文本分类的正确率达到75%,通过因素提取发现了6大类主要的风险因素,即政治风险、经济风险、自然风险、技术风险、管理风险和合作关系风险。在此基础上,对这6方面的风险因素进行了系统分析,并提出了相对应的风险防范对策。
Recent years,the construction of China′s large-scale stadium has been expanding continuously.After many large-scale stadiums are put into use,how to reduce operational risks and ensure their smooth operations has become a key issue that needs to be resolved.This study takes the large-scale stadium as an example.Firstly,based on the combination of text mining and support vector machine,a method for classifying and recognizing factors is established.Then,this method is used to conduct data mining and key factors identification for the existing scholars′research on operating risk factors of large-scale stadiums.Results shows that the correct rate of text classification reaches 75%.Six major risk factors are identified through factor extraction:political risk,economic risk,natural risk,technical risk,management risk and partnership risk.On this basis,a systematic analysis of these six risk factors is conducted,and risk prevention countermeasures are proposed.
作者
刘朝霞
LIU Zhaoxia(School of Physical Education,Chongqing University,Chongqing 400044,China)
出处
《西安体育学院学报》
CSSCI
北大核心
2019年第5期574-579,593,共7页
Journal of Xi'an Physical Education University
基金
中央高校基本科研业务费专项项目资助(106112017CDJXY250001)
关键词
大型体育场馆
文本挖掘
支持向量机
运营风险
large-scale public stadium
text mining
support vector machine
operational risk
