# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 2 Related Work  
### Research on User Emotional Preferences in Product Forms Based on Kansei Engineering  
The theory of Kansei Engineering (KE) [27] laid a solid foundation for acquiring user preferences in product form design. Based on this theory, quantitative techniques such as the Semantic Differential (SD) [28; 29] have been widely adopted. Typically, SD employs questionnaires with pairs of antonyms (e.g., like-dislike, pretty-unattractive, reliable-unreliable, etc.) divided into several levels for consumers to rate [30; 31; 32; 33]. Although this simple questionnaire format can quickly capture user preferences, it may be too broad and vague for designers seeking to understand specific user experiences with products requiring more precise emotional vocabularies to accurately reflect users' emotional needs.  
Consequently, numerous scholars have integrated various methods with KE to map consumers' affective perceptions onto product morphological elements. For instance, [PERSON], [PERSON], et al. [34] constructed a mapping model between consumer psychological image characteristics and SUV car product family shape genes. [PERSON], [PERSON] et al. [35] utilized fuzzy case-based reasoning to create a product style knowledge base for high-heeled shoes. [PERSON] and [PERSON] [36] adopted the Kano model, to investigate user preferences for various vases. [PERSON], [PERSON], et al. [37] used ERP to assess the response of affective words to car design features. [PERSON] [38] verified the effectiveness of a mapping model between style vocabulary and product form design for toy cars. [PERSON] and [PERSON] [39] defined the degree of customer satisfaction for upper limb rehabilitation devices based on adjectives collected from customer reviews. [PERSON] and [PERSON] et al. [40] built a suitable emotional imagery vocabulary for the shape images of bluetooth headsets and computer mice. [PERSON] et al. [41] used KE and knowledge graphs to obtain design requirements for outdoor leisure chairs. [PERSON] et al. [42] proposed a knowledge base for the design of the front face of cars. [PERSON] et al. [43] established the mapping relationship between user imagery needs and product form design elements to meet consumer demand for ceramic bottle design. [PERSON] et al. [44] advanced the field by proposing a shape grammar-based product family DNA for the Audi sedan series, linking explicit morphological features with implicit design intentions. Despite these advancements, most research has focused on detailed design elements and affective perception vocabularies for specific products, making it challenging to simultaneously address multiple types of industrial products. This fragmentation can hinder efficiency in rapidly changing market environments, impacting the entire product design cycle.  
In summary, through a comprehensive review of KE and its applications, it is evident that user preferences for product form have been analyzed from multiple perspectives, providing substantial theoretical support for user preference research. However, to further enhance the accuracy of understanding user preferences and improve design efficiency, more effective tools and methodologies are required to integrate these preferences into practical applications across a wide range of industrial products. This study combinesKE with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to acquire unified user affective perceptions across various product forms, thereby enabling designers to better comprehend the semantic needs of different industrial products and transform abstract user emotions into concrete design guidelines, ultimately enhancing design efficiency.