## 2 Literature Review
### Ke  
KE studies users' sensations and impressions, aiming to effectively measure people's psychological perceptions of products and translate them into design characteristics. It was initially introduced by the Japanese scholar [PERSON] [25]. Currently, KE research mainly focuses on two directions. In the first one, during the user-oriented innovative design process, KE tools are used to measure users' psychology, and the precise measurement of users' perceptions guides the design. Some scholars conducted a form design study on ear thermometers, employing a combination of KE and AHP to measure and make decisions regarding users' emotional needs. Then, they established a mapping model between requirements and alternative solutions, selected the optimal scheme for detailed design, and effectively achieved a design that meets users' emotional demands [5]. Other scholars effectively captured users' emotional needs using an improved KE method and combined it with the Kano model for the innovative design of sustainable service, demonstrating the effectiveness of KE in capturing and applying user needs in service design innovation [26]. In the second one, during the product development process, KE is adopted as an indicator that influences user satisfaction. By combining it with other innovative methods in R&D, the efficiency of product development is improved. KE and knowledge graph were proposed as a research framework, which mines consumers' emotional needs and constructs a knowledge management model oriented towards these needs. Then, it identified the knowledge with mapping relationships in professional designers' knowledge systems and finally conducted an effectiveness study with chair design knowledge as an example [27]. KE and TRIZ were proposed as an integrated method for researching the recycling of ecologically innovative products, which has not only met user needs but also achieved the goal of environmental protection, fully demonstrating the effectiveness of KE in capturing CRs during product development [28]. In recent years, with the development of internet technology, traditional questionnaires and survey methods have had limitations due to the small data they collect. Many scholars have started to research the comprehensiveness and feasibility of user reviews and data mining in CR surveys. A data mining technique was developed by using online reviews to categorize consumer emotions and applied heuristic deep learning to KE, successfully translating extensive user emotional data into design parameters and improving the objectivity of design results [29].  
In summary, to help SMEs quickly obtain and accurately filter customer emotional requirements, KE, as a user-centered approach, can effectively capture consumers' emotions and perceptions, transforming them into design elements. During product development, this method requires the integration of data mining, engineering management, and mathematical algorithms to objectively measure and accurately prioritize user emotions, assisting enterprises in identifying key emotional needs. At the same time, CRs need to form a quantifiable mapping relationship with design elements to accurately pinpoint design objectives.