The Maclaurin symmetric mean (MSM) and dual Maclaurin symmetric mean (DMSM) operators are two aggregation operators to aggregate the q-rung orthopair fuzzy numbers (q-ROFNs) into a single element. The complex q-rung orthopair fuzzy framework is more effective to describe fuzzy information in real decision-making problems. The complex q-rung orthopair fuzzy sets (Cq-ROFSs) are more superior to the complex intuitionistic fuzzy sets (CIFSs) and complex pythagorean fuzzy sets (CPFSs) to cope with uncertain and difficult information in the environment of fuzzy set theory. Thus, this manuscript proposes some complex q-rung orthopair fuzzy Maclaurin symmetric mean (Cq-ROFMSM), complex q-rung orthopair fuzzy weighted Maclaurin symmetric mean (Cq-ROFWMSM), complex q-rung orthopair fuzzy dual Maclaurin symmetric mean (Cq-ROFDMSM) and complex q-rung orthopair fuzzy weighted dual Maclaurin symmetric mean (Cq-ROFWDMSM) operators. Moreover, some properties and special cases of our proposed methods are also introduced. Then we present a multi-attributive group decision-making based on proposed methods. Further, a numerical example is provided to illustrate the flexibility and accuracy of the proposed operators. Last, the proposed methods are compared with existing methods to examine the best emerging technology enterprises.

Maclaurin对称均值（MSM）和对偶Maclaurin对称均值（DMSM）运算符是将q阶邻对模糊数（q-ROFN）聚合为单个元素的两个聚合算符。复杂的q阶正交对对模糊框架在描述实际决策问题时更有效地描述模糊信息。复杂的q阶正交对对模糊集（Cq-ROFS）优于复杂的直觉模糊集（CIFS）和复杂的勾股模糊集（CPFS）来处理模糊集理论环境中的不确定和困难信息。因此，该手稿提出了一些复杂的q阶邻对模糊Maclaurin对称均值（Cq-ROFMSM），复杂的q阶邻对模糊加权Maclaurin对称均值（Cq-ROFWMSM），复杂q阶邻对对模糊对偶Maclaurin对称均值（Cq-ROFDMSM）和复杂q阶对对模糊对偶Maclaurin对称均值（Cq-ROFWDMSM）算符。此外，还介绍了我们提出的方法的一些特性和特殊情况。然后，基于提出的方法，提出了一种多属性的群体决策方法。此外，提供了一个数值示例来说明所提出的算子的灵活性和准确性。最后，将提出的方法与现有方法进行比较，以研究最佳新兴技术企业。提供了一个数值示例来说明所提出的算子的灵活性和准确性。最后，将提出的方法与现有方法进行比较，以研究最佳新兴技术企业。提供了一个数值示例来说明所提出的算子的灵活性和准确性。最后，将提出的方法与现有方法进行比较，以研究最佳新兴技术企业。