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Value perception impact and countermeasures analysis of new energy vehicle purchase behavior based on consumer level user review big data mining

机译：基于消费者级别用户评论大数据挖掘新能源车辆采购行为的价值感知对策及对策分析大数据挖掘

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The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.
机译：新能源汽车的发展是由消费者驱动分不开的。因此，从消费者的个人层面探讨购买行为的影响因素，有助于企业采取相应的销售策略和政府采取相关政策。根据消费者的个性化水平，构建从评论文章了新能源汽车的购买行为预测模型，并探讨了消费者个人因素对新能源汽车的购买行为的预测作用。首先，本文提出了消费者个体层面因素的定量方法，它结合了字的口碑评论与统计。在该方法中，word2vec用于列车字矢量中字的口语料库以矿初始关键字，和核心关键字通过统计相关分析选择。其次，根据消费者的个人层面的核心关键词，gbdt构建模型来预测新能源汽车的购买行为。结果表明，正确地预测消费者的购买行为的概率超过72％。

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