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基于大数据环境下面孔空想性错视对产品造型影响研究

Face Pareidolia on Product Modeling Based on Big Data Environment
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摘要 目的探究面孔空想性错视现象是否会对产品造型的审美满意度产生影响。方法采用实证研究的方式,运用大数据网络爬虫程序提取产品的语义信息、通过词频分析方法构建语义差分量表,并分别建立三组对照组实验对被试者进行问卷调查,最后分析三组实验数据得出研究结论。结果实验发现被试者在不考虑产品价格、功能的情况下,审美偏好更倾向于能够引起面孔空想性错视的产品造型。结论在产品造型设计中运用面孔空想性错视现象,可以在一定程度上引起网购消费者的注意力,在传达产品情绪化特征的同时,满足其功能和审美需求,从而对产品造型审美满意度产生正向影响。运用面孔空想性错视现象是满足用户需求、提高网购消费者购买意愿的有效设计途径和方法。 The paper aims to explore whether the phenomenon of face pareidolia will affect the aesthetic satisfaction of product modeling.In the way of empirical research,the big data web crawler program was used to extract the semantic information of products,and the semantic difference subscale was constructed by word frequency analysis.Three groups of control experiments were established respectively,and the subjects were investigated by questionnaire.Finally,the three groups of experimental data were analyzed to draw the research conclusion.The experiment found that without con-sidering the product price and function,the subjects'aesthetic preference was more inclined to the product shape that could cause the face pareidolia.The application of face pareidolia in product modeling design can attract the attention of online shopping consumers to a certain extent,convey the emotional characteristics of products,meet their functional and aesthetic needs,and have a positive impact on the aesthetic satisfaction of product modeling.The use of face pareidolia is an effective design way and method to meet the needs of users and improve the purchase intention of online shopping consumers.
作者 王绍东 邢亚龙 张馨友 WANG Shao-dong;XING Ya-long;ZHANG Xin-you(International Design Art College,Inner Mongolia Normal University,Hohhot 010022,China;Faculty of Innovation and Design,City University of Macao,Macao 999078,China)
出处 《包装工程》 CAS 北大核心 2022年第18期240-246,326,共8页 Packaging Engineering
基金 2018年内蒙古哲学社会科学规划项目《特色小镇助力内蒙古非遗保护与传承实践研究》(2018NDB091) 澳门基金会资助研究项目(MF2037)。
关键词 面孔空想性错视 大数据 网络爬虫程序 词频分析 语义差分量表 face pareidolia big data web crawler word frequency analysis semantic difference component table
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