A Data-Driven Multi-Criteria Evaluation Framework for the Design Optimization of Children’s Furniture

Abstract

To enhance the scientific rigor and user alignment of children’s furniture design, this study proposes a data-driven multi-criteria evaluation framework for design optimization. Focusing on wooden seating products for children, online product reviews were collected and preprocessed using Python and the Jieba word segmentation tool to extract authentic user needs. An evaluation index system was established by filtering indicators through expert focus group discussions and the coefficient of variation method. During the weighting phase, subjective weights were derived using an improved Analytic Hierarchy Process (AHP), while objective weights were calculated via the CRITIC method. A game-theoretic approach was employed to integrate both into a composite weight vector. Finally, the TOPSIS–RSR model was applied to rank and classify the performance levels of four wooden children's seating designs. Based on the results, specific design guidance strategies were proposed. The proposed framework effectively captures user requirements, balances subjective and objective information, and provides a clear decision-making pathway for selecting optimal design solutions. The study not only advances theoretical research but also offers practical guidance for design, with strong potential for extension to other furniture categories and resource-driven product design domains.