Network meta-analysis aims to integrate direct and indirect evidence, make a comprehensive comparison and in-depth analysis of three or more interventions and treatments, compare and rank the advantages and disadvantages of different treatment measures, so as to provide strong evidence for decision-making. However, there may be some bias in the process of making network meta-analysis, analyzing data and interpreting results. Therefore, accurate assessment and proper handling of the risks of bias in network meta-analysis is conductive to improve the quality of decision-making and promote the achievement of good clinical outcomes. At present, the number of published network meta-analysis has increased significantly globally, but the quality remains to be improved, and there is a lack of assessment tools for risks of bias in network meta-analysis. In 2025, Canadian scholar Carole Lunny and colleagues developed The Risk of Bias in Network Meta-Analysis (RoB NMA) tool for evaluating the risk of bias in network meta-analysis and published it in the BMJ, which is important to reduce the bias in network meta-analysis and promote optimal clinical decision-making. This study will interpret it with examples, aiming to help researchers better understand and apply this evaluation tool.