Socio-Economic Development in Chinese Educational Systems Based on Robust Aggregation Operators for Managing Complex q-Rung Orthopair Fuzzy Uncertainty and Their Applications

Sustainable development of the Chinese education sector demands a comprehensive investment strategy that integrates public-private partnerships, early childhood education, community engagement, teacher capacity building, curriculum relevance, equitable resource distribution, and continuous quality monitoring. Such an inclusive framework strengthens human capital and supports long-term economic growth. To effectively manage uncertainty and vagueness in multi-attribute group decision-making (MAGDM) problems arising from such investment evaluations, this study aims to develop novel aggregation operators within the complex q-rung orthopair fuzzy (Cq-ROF) environment. For this purpose, newly defined algebraic complex operational laws are introduced to construct the complex q-rung orthopair fuzzy weighted arithmetic mean (Cq-ROFWAM), ordered weighted arithmetic mean (Cq-ROFOWAM), weighted geometric mean (Cq-ROFWGM), and ordered weighted geometric mean (Cq-ROFOWGM) operators. These operators provide a flexible and robust framework for aggregating uncertain and complex evaluation information while effectively capturing decision-makers' preferences. Based on these operators, a novel MAGDM model is developed to support investment assessment in the Chinese education sector. An illustrative case study demonstrates the applicability and practicality of the proposed approach. Furthermore, comparative and numerical analyses verify that the proposed model produces stable and consistent alternative rankings, accurately identifies the optimal investment strategy, and offers reliable decision support. The main contributions of this work include the introduction of new Cq-ROF aggregation operators based on algebraic complex operational laws, the development of a comprehensive MAGDM framework, and the provision of a flexible and reliable decision-making methodology that outperforms existing approaches in handling uncertain information.