Abstract: Improving the quality of export products is an important issue that China’s furniture industry must address. This article first uses spatial analysis to identify the distribution pattern and evolution characteristics of China's furniture industry clusters, establishes a comprehensive evaluation model to measure the development level of these clusters, and measures the export product quality of China's furniture from the perspective of market concentration. Second, considering both the positive and negative effects of industrial clusters, this article analyzes the mechanism through which industrial cluster development affects export product quality and proposes hypotheses. Using data from 2014 to 2021, it then establishes a dynamic panel data model to examine the effects and pathways of this influence. The results show that: China's inter-provincial furniture industry clusters are mainly distributed across 15 provinces (autonomous regions and municipalities) in East, South, North, Southwest, and Northeast China; except for the clusters in Shandong and Liaoning, the maturity of most other clusters fluctuated upward but remained at the growth stage, and only those in Shanghai, Tianjin, Zhejiang, and Guangdong entered the mature stage; R&D expenditure was the most important influencing factor; the export product quality of China’s furniture improved slightly amid fluctuations, rising relatively quickly from 2015 to 2018 and declining slightly from 2019 to 2021; clusters in coastal areas had higher export product quality and smaller fluctuation, whereas clusters in inland areas improved faster but fluctuated more; industrial cluster development was conducive to improving export product quality, no negative effect of over-agglomeration had yet emerged, and the influence exhibited a threshold effect. Finally, this article proposes recommendations: strengthening policy support and differentiated resource allocation, increasing investment in innovation and R&D, establishing cross-regional cooperation and exchange mechanisms, piloting industrial integration and replacing simple scale control with dynamic monitoring, guarding against potential over-agglomeration and adopting differentiated policies according to the cluster life cycle, and carrying out targeted optimization and brand building.