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Research on New Energy Vehicle User Needs Identification and Intervention Strategy Based on Data Mining

Author(s)

Yueli Qin, Fucheng Wang*, Yuting Qin, Yaqi Zhang, Zhuangyang Qin

Affiliation(s)

Guilin University of Electronic Technology, Guilin, Guangxi, China *Corresponding Author.

Abstract

This study aims to identify user needs and propose intervention strategies for new energy vehicles (NEVs) through data mining techniques. The study analyzed online feedback from NEV users, focusing on 77,052 high-quality reviews from 11 leading domestic manufacturers. Using the SnowNLP tool for sentiment analysis, the results showed that positive sentiment significantly outweighed negative sentiment, indicating a generally favorable user perception of NEVs. Latent Dirichlet Allocation (LDA) topic modeling revealed key areas of user feedback, including space design, dynamic performance, interior quality, control experience, comfort, seat design, driving range, pricing, vehicle noise, and more. Users expressed high satisfaction with spatial design, acceleration, interior quality, and handling, while concerns were raised regarding spatial comfort, seat design, driving range, price discounts, and in-car sound insulation. Based on the "push-pull anchor" framework, the study proposes specific intervention strategies from both macro and micro perspectives. Macro strategies include fiscal subsidies, tax incentives, and infrastructure development to enhance charging convenience and promote environmental awareness. Micro strategies focus on improving driving range, enhancing intelligent features, optimizing interior design, enhancing after-sales service, and strengthening user interaction. These strategies aim to address user needs, improve consumer satisfaction, and promote the healthy development of the NEV market. Future research could further validate these strategies through empirical studies and real-world market data.

Keywords

New Energy Vehicles; Data Mining; User Demand Identification; Intervention Strategy

References

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