一种新的拓展三维机会挖掘算法：基于新能源汽车市场的机会挖掘

doi:10.16381/j.cnki.issn1003-207x.2024.0790

Abstract

Abstract:

In the increasingly competitive field of new energy vehicle manufacturing， market opportunities must be accurately grasped for new energy vehicle manufacturers to gain competitive advantages. The existing market opportunity mining algorithms mainly analyze potential consumer demand satisfaction and demand importance based on online comment information to explore potential demand as market opportunities. However， critical limitations are exhibited in these methods： interactive information is neglected conveyed by consumers when measuring demand satisfaction， and multiple influencing factors are not considered and their interrelationships when assessing demand importance， resulting in distorted reflection of genuine consumer needs through online reviews. Furthermore， temporal dynamics are overlooked in identifying potential demands which may hinder the ability to dynamically capture evolving market opportunities. Based on the above shortcomings， a new extended 3D opportunity mining algorithm is proposed and applies it to identify opportunities in the new energy vehicle market： firstly， the satisfaction measure is proposed based on the identification of false reviews and the consideration of interactive information； secondly， the importance measure is proposed by combining the Choquet integral with the effective frequency of demand， the influence of demand on satisfaction， and the Baidu index， etc； and lastly， based on the satisfaction and importance， satisfaction measure is proposed based on the interaction information of consumers. Finally， on the basis of satisfaction and importance， a measure of demand potential by considering the time factor is proposed， which extends the traditional opportunity algorithm to the three-dimensional space of "potential-demand satisfaction-demand importance". By applying the method framework to 7，606 text reviews of the Tang new energy vehicle series from 2015 to 2022， the ranking of consumer attention to demands and improvement suggestions are obtained. And the changes in demand importance and demand satisfaction within different time periods are analyzed. The results of comparative analysis demonstrate the effectiveness and necessity of the new method. In future research， a set of comprehensive indicators and systems are provided for measuring importance， and a method is provided for measuring demand potential considering the time factor. In practice， market opportunities for new energy vehicles can be accurately and effectively identified， and enterprises are helped to upgrade their vehicle models in a targeted manner.

Key words: new energy vehicle, opportunity mining algorithm, online reviews, consumer demand, product improvement

CLC Number:

C934

Meng Zhao,Hailong Wang,Wenshuai Wu, et al. New Expanded 3D Opportunity Mining Algorithm: Opportunity Mining Based on the New Energy Vehicle Market[J]. Chinese Journal of Management Science, 2026, 34(8): 356-368.

Figures/Tables 9

References 40

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需求	需求关注点	情感得分	优先级	需求关注点	情感得分	优先级
续航	提升充电速度	0.3909	6	优化底盘调校	0.3570	8
	增大电池容量	0.3992	7	减小刹车异响	0.1554	3
	减少能耗	0.3865	5	减小车身异响	0.1095	1
	减少油耗	0.3171	3	减小底盘异响	0.2283	5
	提高续航里程	0.3505	4	减少车身抖动	0.3705	9
	减小馈电顿挫感	0.2331	1	提高减震效果	0.4147	11
	减小馈电噪音	0.3068	2	增强空调舒适性	0.1490	2
舒适性	增加通风座椅	0.3069	7	增加备胎	0.2033	4
	提高座椅软度	0.3811	10	防止多媒体死机并提高识别能力	0.5690	12
	增强座椅包裹性	0.2671	6	防止多媒体死机并提高识别能力	0.5690	12

序号	需求	IPA模型	KANO模型	本文方法
1	空间	低优先级	吸引型	继续保持
2	动力	低优先级	吸引型	继续保持
3	续航	优先改善	期望型	重点改进
4	操控	供给过剩	吸引型	减少投入
5	舒适性	优先改善	期望型	重点改进
6	外观	继续保持	基本型	减少投入
7	内饰	供给过度	无差异型	减少投入
8	性价比	供给过度	无差异型	减少投入

New Expanded 3D Opportunity Mining Algorithm: Opportunity Mining Based on the New Energy Vehicle Market

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