## 1 Introduction  
In the 21 st century, battery electric vehicles (BEVs) driven by electric powertrains [1] are at the forefront of the transition to sustainable transportation. The International Energy Agency's (IEA's) Global Electric Vehicle Outlook 2024 indicated a total of 14 million electric vehicle sales in 2023, with China commanding 56.8% of the global BEV market, underscoring the critical nature of researching the Chinese battery electric vehicles' user experience (BEVUX) [2].  
Despite an abundance of research on BEV performance and promotion, a systematic exploration of the multidimensional aspects of user experience (UX) remains sparse. UX constitutes a multidimensional construct that includes vehicle performance, charging convenience, after-sales service, user interface design, and brand image. These factors, both individually and in interaction, sculpt the overall user perception. Consequently, this study introduces an interdisciplinary framework aimed at identifying and analyzing the multidimensional factors that influence BEVUX in China, as well as exploring their interrelations and interactions.  
User-generated content (UGC) serves as a valuable representation of user experience, capturing its multidimensional attributes. Platforms such as Zhihu, where users openly share their in-depth experiences and perspectives, provide a rich dataset for understanding the complexities of BEVUX. By analyzing UGC, this research aims to extract insights that go beyond the scope of traditional surveys, effectively capturing nuanced user sentiments and multifaceted interactions with BEVs. Therefore, the use of UGC-based data, such as from Zhihu, is crucial to comprehensively reflect the real-world user experience and its various dimensions, making multidimensional text mining a meaningful approach.  
The research questions are delineated below:  
**RQ1:** What comprehensive methodology can be employed to systematically evaluate the multidimensional factors affecting BEVUX in China?  
**RQ2:** What are the emotional propensities of users toward these factors, and how do these interact and correlate?  
The primary goal is to dissect and quantify the pivotal factors influencing BEVUX in China and their emotional associations through an interdisciplinary lens. Specifically, this research utilizes the latent Dirichlet allocation (LDA) model and entropy weight method (EWM) to identify and prioritize factors influencing BEVUX from UGC, employing domain-adaptive sentiment analysis (DASA) and co-occurrence network analysis (CONA) to quantify users' emotional inclinations and their interconnections.  
The study's innovativeness and practical applicability are evident in the following aspects:  
1. Methodological innovation: pioneering an interdisciplinary approach that integrates the LDA model, EWM, DASA, and CONA, thereby broadening the scope of UX research.
2. Data-driven: utilizing UGC from Zhihu, a rich and diverse dataset, offers a nuanced representation of user experiences that surpasses traditional surveys or interviews.
3. Addressing complexity: through multidimensional analysis, this study skillfully decodes the complexities of UX, revealing the salient factors and their dynamics.
4. Practical value: the findings provide strategic insights for BEV manufacturers to enhance design and services, elevate user satisfaction, and guide policymakers in formulating effective strategies.  
By elucidating the key factors and their interrelationships influencing UX, this study contributes robust empirical evidence and a robust theoretical foundation to the electric vehicle sector.