Numerous decision-making problems are expressed in the form of uncertain linguistic variables in real life. Expressing qualitative decision-making information in quantitative form can make the decision making process clearer and simpler. In this paper, we transform the 2-dimension uncertain linguistic variable into a cloud model which converses the qualitative information to the quantitative information and aims for solving the multiple attribute group decision making problems. Firstly, we convert 2-dimension uncertain linguistic variables to 2-dimension clouds. Then, we compare the importance between 2-dimension information and propose a method to transform the 2-dimension cloud model of uncertain linguistic variables into 1-dimension cloud model. Meanwhile, we aggregate 2-dimension uncertain experts’ preferences and linguistic attributes variables on the basis of CCI operator. In addition, we determine the best alternative by comparing the distance between each alternative cloud and the ideal cloud. Finally, a comparable example is used to illustrate the advantage and feasibility of the proposed method. The cloud model can realize the mutual transformation between qualitative information and quantitative information, and integrate the 2-dimension uncertain linguistic variables by comparing the importance of the 2-dimension information, so as to make the decision-making process clearer.

许多决策问题在现实生活中以不确定的语言变量的形式表达。以定量的形式表达定性的决策信息，可以使决策过程更加清晰和简单。在本文中，我们将二维不确定语言变量转化为云模型，该模型将定性信息转化为定量信息，旨在解决多属性群决策问题。首先，我们将二维不确定语言变量转换为二维云。然后，我们比较了二维信息之间的重要性，并提出了一种将不确定语言变量的二维云模型转化为一维云模型的方法。同时，我们在 CCI 算子的基础上聚合了二维不确定专家的偏好和语言属性变量。此外，我们通过比较每个替代云与理想云之间的距离来确定最佳替代。最后，通过一个可比较的例子来说明所提出方法的优点和可行性。云模型可以实现定性信息和定量信息的相互转换，通过比较二维信息的重要性，整合二维不确定的语言变量，使决策过程更加清晰。一个可比较的例子被用来说明所提出的方法的优点和可行性。云模型可以实现定性信息和定量信息的相互转换，通过比较二维信息的重要性，整合二维不确定的语言变量，使决策过程更加清晰。一个可比较的例子被用来说明所提出的方法的优点和可行性。云模型可以实现定性信息和定量信息的相互转换，通过比较二维信息的重要性，整合二维不确定的语言变量，使决策过程更加清晰。