Data-Driven Furniture Reuse and Circulation Management

Abstract

With the increasing pressure of resource depletion and environmental pollution, improving the efficiency of household asset circulation has become a critical issue in sustainable consumption systems. Traditional idle goods trading platforms mainly focus on simple information matching, which fails to effectively address users’ decision uncertainty, service fragmentation, and low reuse rates This study proposes an intelligent circulation management framework for second-hand furniture based on behavioral modeling and data-driven service optimization. By integrating large-scale questionnaire surveys, usage behavior tracking, and preference mining techniques, a multi-dimensional user interaction model is constructed to characterize individual decision patterns and reuse intentions. On this basis, a hybrid service architecture combining user behavior prediction, lifecycle assessment, and value redistribution mechanisms is developed. The proposed framework introduces a dynamic matching and incentive model to optimize transaction efficiency and promote long-term user engagement. In addition, a digital prototype platform is implemented to validate the effectiveness of the proposed approach in real application scenarios. Experimental evaluation indicates that the proposed system significantly improves asset utilization efficiency, user satisfaction, and environmental benefit indicators. This research provides a new technical pathway for integrating intelligent service systems with circular economy strategies in the furniture industry.