Brucellosis, a zoonotic disease primarily transmitted through livestock, poses significant health and economic challenges globally. Its increasing incidence in China highlights critical gaps in current control measures, particularly in regions with high livestock movement, where domestic ruminants trade networks elevate the risk of disease transmission. Traditional value chain analyses often provide descriptive mappings of these networks but lack quantitative methods to assess specific risk levels. This study integrates value chain with quantitative risk assessment using Monte Carlo simulations to evaluate the risk of brucellosis introduction via cattle imports in Zhijiang. We comprehensively mapped the local cattle value chain, identifying key stakeholders, risk nodes, and critical pathways of infection. Utilizing a stochastic scenario tree model based on local data, we estimated the likelihood of infected cattle entering the region, revealing that unregulated brokers and inadequate quarantine measures significantly contribute to the risk. Our sensitivity analysis confirmed vulnerabilities in existing biosecurity protocols, emphasizing the need for enhanced quarantine measures and reliable testing at high-risk points in cattle trade. By combining value chain insights with quantitative analysis, this study effectively identifies critical vulnerabilities within livestock networks. Our findings lay the groundwork for developing targeted brucellosis prevention strategies in Zhijiang, underscoring the urgent need for strengthened biosecurity and risk management practices.