Abstract:

To overcome traditional design methods’ inability to capture differentiated emotional needs, a new approach combining user cluster theory and swarm intelligence was developed. Based on online user review data, the RCLA model was used to identify high-value user groups; natural language processing and social network analysis were combined to identify heterogeneous user clusters; and the entropy weighting method was employed to quantify the weight of emotional needs. Second, morphological analysis was used to deconstruct product design elements and establish a coding system. Subsequently, type I quantification theory was used to calculate the contribution of design elements to emotional needs, and an ant colony algorithm was introduced to achieve intelligent configuration of design elements, combined with a user feedback mechanism to complete iterative optimization of the design. Using a vacuum cleaner as a case study, a semantic differential scale was employed to conduct satisfaction evaluations and validate the design solutions. The results demonstrated that the initial optimized solution achieved significantly greater satisfaction of emotional needs compared to traditional design samples. The final solution, refined through user feedback iterations further identified subtle differences in needs within clusters, continuously enhancing the design’s user adaptability and satisfaction. The study showed that this data-driven emotional design method, integrating intelligent algorithms, overcame the limitations of traditional small-sample analysis and offer new insights into product innovation under dynamic demand.

Key words: heterogeneous user cluster, product emotional design, Kansei engineering, ant colony optimization, social network analysis