<p>The rapid evolution of electronic commerce (e-commerce) has reshaped traditional industries and marketing. In this context, this study addresses the challenge of seller-buyer matching congestion on e-commerce platforms, particularly in used car trading. To model the uncertainty and heterogeneity in user preferences, this study employs interval numbers and prospect theory to calculate the gains and losses of buyers and sellers, thereby capturing both ambiguous expectations and psychological behaviors. A priority-based matching framework is then proposed to adjust satisfaction scores according to platform-defined user priorities and individual preferences. This adjustment allows the platform to promote high-priority participants and reduce waiting times without compromising overall performance. A stable matching model is formulated to maximize bilateral satisfaction while ensuring implementable outcomes simultaneously. A case study and extensive simulations can prove the proposed method is effective in improving satisfaction, reducing average waiting times, and maintaining matching stability. The study contributes to existing literature by combining uncertainty preferences with priority-based matching to enhance theoretical understanding and practical performance of two-sided matching in congested environments.</p>