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Finance  2024 

基于文本挖掘技术的农村新能源汽车购买决策影响因素分析
Analysis on Influencing Factors of Rural New Energy Vehicles Purchase Decision Based on Text Mining Technology

DOI: 10.12677/FIN.2024.141002, PP. 7-13

Keywords: 新能源汽车,TF-IDF算法,农村地区
New Energy Vehicles
, TF-IDF Algorithm, Rural Areas

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Abstract:

为促进新能源汽车产业发展和环境保护,中国政府实施了新能源汽车下乡政策,鼓励农村地区消费者购买新能源汽车。本文旨在探讨影响中国农村地区电动汽车采用的因素,采用了TF-IDF算法对农村消费者购车评论的特征词进行提取和词频统计,以分析消费者的购车决策因素。通过对农村电动汽车消费者的复合评论进行情感波动的分析,不仅可以拓展消费者感知价值理论的研究深度,还可以为厂商和消费者提供内容情报和数据参考,协助他们调整生产行为和优化购买决策。研究结果表明,在农村市场,消费者更注重汽车价格、空间、维修成本、驾驶感受和油耗等因素,新能源汽车占有量与燃油车价格呈正相关,并受到羊群效应的影响。研究结果对于推动新能源汽车在农村市场的推广和普及具有重要意义。
In order to promote the development of new energy vehicle industry and environmental protection, the Chinese government has implemented a policy of new energy vehicles going to the countryside to encourage consumers in rural areas to buy new energy vehicles. The purpose of this paper is to explore the factors that affect the adoption of electric vehicles in rural areas of China. TF-IDF algorithm is used to extract the characteristic words of rural consumers’ car purchase comments and make word frequency statistics, so as to analyze the factors of consumers’ car purchase decision. By analyzing the emotional fluctuation of rural electric vehicle consumers’ composite comments, we can not only expand the research depth of consumer perceived value theory, but also provide content information and data reference for manufacturers and consumers to help them adjust their production behavior and optimize their purchase decisions. The research results show that in rural markets, consumers pay more attention to factors such as car price, space, maintenance cost, driv-ing experience and fuel consumption. The research results are of great significance for promoting the promotion and popularization of new energy vehicles in rural markets.

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