Large-scale group decision-making process has received an increasing attention in recent years. After making the general survey of the existing large-scale group decision-making methods, we have found that: 1) consistency threshold value of hesitant fuzzy linguistic preference relation is fixed in traditional consistency measures; 2) the clustering process of LSGDM does not consider the similar relationship between different evaluation information and the information quality simultaneously. Thus, in order to tackle the above issues and describe the hesitancy of experts in the decision-making process, the paper proposes a hesitant fuzzy linguistic bi-objective clustering method considering consensus and information entropy for tackling large-scale group decision-making problems. Firstly, a selection procedure for preference information is developed to quickly select suitable experts who meet the consistency requirements. Then, a bi-objective clustering method based on the group consensus degree indicator and group information entropy indicator is proposed to divide the experts into different clusters, considering the similar relationship and the quality of evaluation information simultaneously. After that, comprehensive preference information and the overall ranking of alternatives can be obtained. In the end, an illustrative example of choosing the optimal way to protect the personal information while defending against COVID-19 and some comparative study show that the proposed method is valid for large-scale group decision-making problems and has good performance and strong robustness.

近年来，大型团体决策过程受到越来越多的关注。在对现有的大规模群体决策方法进行总体研究后，我们发现：1）传统的一致性测度中，犹豫的模糊语言偏好关系的一致性阈值是固定的；2）LSGDM的聚类过程没有同时考虑不同评估信息和信息质量之间的相似关系。因此，为了解决上述问题并描述专家在决策过程中的犹豫，本文提出了一种基于共识和信息熵的犹豫模糊语言双目标聚类方法，用于解决大规模群体决策问题。首先，开发偏好信息的选择程序，以快速选择满足一致性要求的合适专家。然后，提出了一种基于群体共识度指标和群体信息熵指标的双目标聚类方法，将专家分为不同的聚类，同时考虑相似的关系和评估信息的质量。之后，可以获得全面的偏好信息和替代方案的总体排名。最后，举例说明了在抵御COVID-19的同时选择最佳方式保护个人信息的方法，并进行了比较研究，结果表明，该方法适用于大规模群体决策问题，具有良好的性能和较强的鲁棒性。 。