Design Element Analysis Driven by Attribute Networks: A Hybrid Method for Identifying Critical Product Design Attributes

In today’s consumer-oriented market environment, enterprises face increasingly intense competition, making the identification of key factors in demand-driven product design critically important. Identifying critical product design elements facilitates the optimal allocation of design resources and enhances product competitiveness to better meet user expectations. This study proposes an attribute-network-based method for identifying key product design elements grounded in complex network theory, providing a novel quantitative approach for investigating the mapping relationship between user cognition and design elements within the field of Kansei engineering. First, products are modeled as nodes, and edges are established based on thresholded attribute similarity between products to construct a product attribute network. Second, community detection algorithms are applied to partition the constructed network, revealing the intrinsic association strength among product attributes and enabling the identification of key products within each cluster. Subsequently, a random forest method is employed to extract the core product attributes that influence cluster formation, yielding attribute importance rankings, which are further validated using the XGBoost algorithm to ensure robustness. Finally, a Bluetooth speaker case study is conducted to verify the effectiveness and feasibility of the proposed method for identifying key product design factors. The results demonstrate that the proposed approach can effectively identify key product design factors and rank the critical factors influencing the product design process, achieving high accuracy and practical applicability.