Article(id=1317501497525514245, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260172, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792046435004, onlineDateStr=2026-10-15, pubDate=1791129600000, pubDateStr=2026-10-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792046435004, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792046435004, creator=13701087609, updateTime=1792046435004, updator=13701087609, issue=Issue{id=1317501484401541130, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='10', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1791129600000', pubDateStr='2026-10-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792046431875, creator='13701087609', updateTime=1792121205555, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317815108026356240, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317815108026356241, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=44, endPage=53, ext={EN=ArticleExt(id=1317501497785561094, articleId=1317501497525514245, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on Evolution of Intelligent Electric Vehicle Design Language Based on Multimodal Large Model and Kansei Engineering, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Against the backdrop of rapid iteration in intelligent electric vehicles, design language has emerged as a core carrier for brand identification and user emotional interaction. However, the dynamic coupling between design language and users' perceptual cognition lacks large-scale data support and objective quantitative tools. Taking BYD and Tesla as typical Eastern-Western comparative cases, this study constructs a 2020—2026 multimodal dataset consisting of 125 standardized vehicle images and 6 596 valid user comments, generating over 140 000 image-text pairs. With image-text features extracted via Chinese-CLIP, combined with Kansei engineering factor analysis and Analytic Hierarchy Process (AHP), this paper quantitatively analyzes the temporal evolution of design language and the trend of perceptual imagery matching. The results reveal obvious brand divergence in users' perceptual vocabulary: BYD is dominated by “stability” and “spaciousness”, while Tesla centers on “technological sense” and “futuristic sense”. The image-text matching degree of both brands declines slowly, with a sharper drop for Tesla, indicating diversified user aesthetics amid market maturity. At the factor level, BYD shows differentiated factor trends: its perception of technological futurism continuously weakens, whereas the imagery of luxury and refinement rebounds after bottoming out. Tesla's “technological futurism” factor remains high yet fluctuates, uncovering distinct evolutionary logics between Eastern and Western automotive brands.

, authors=Meng Tang¹, Tian Guan¹, Ting Han^{1, 2}, authorsList=Meng Tang, Tian Guan, Ting Han, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1317501498133688327, articleId=1317501497525514245, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于多模态大模型与感性工学的智能电动汽车设计语言演化规律研究, columnId=1317501485198458892, journalTitle=汽车文摘, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

在智能电动汽车快速迭代背景下，设计语言已成为品牌识别与用户情感交互的核心载体，但其与用户感性认知的动态耦合关系，长期缺少大规模数据支撑与客观量化手段。选取比亚迪与特斯拉作为中西对比研究案例，构建2020—2026年多模态数据集（包含125张标准化车型图片、6 596条有效评论，形成14万余组图文配对）。依托Chinese-CLIP提取图文特征，融合感性工学因子分析与层次分析法（AHP），量化解析两品牌设计语言时序演化与感性意象匹配趋势。结果表明：用户感性词汇存在明显的品牌差异，比亚迪的核心标签为“稳重”“空间”，特斯拉的核心标签为“科技感”“未来感”；两个品牌图文匹配度均缓慢下行，特斯拉降幅更大，体现市场成熟下用户审美趋于多元；因子维度上，比亚迪感性因子走势分化，科技未来感知持续减弱，豪华精致意象触底反弹；特斯拉“科技未来”因子维持高位但出现波动，反映东西方车企差异化设计演化逻辑。

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