<p>The complex q-rung orthopair fuzzy (CqROF) set is a robust mathematical model that handles two dimensional data with perfection. However, existing multi-criteria decision-making (MCDM) methods based on CqROF data do not account for the interconnections among multiple criteria and are unable to mitigate the impact of extreme values. To address these issues, this paper proposes novel operators, namely the CqROF partitioned Maclaurin symmetric mean (CqROFPMSM) and the CqROF weighted partitioned Maclaurin symmetric mean (CqROFWPMSM), designed to handle scenarios where criteria are divided into distinct parts with interconnections among multiple criteria within the same part. The advantageous characteristics of the proposed operators are thoroughly examined. To alleviate the negative impact of extreme evaluation values on the aggregated outcome, additional operators, the CqROF power partitioned Maclaurin symmetric mean (CqROFPPMSM) and the CqROF weighted power partitioned Maclaurin symmetric mean (CqROFWPPMSM), are formulated. Furthermore, the indifference threshold-based attribute ratio analysis (ITARA) method is extended under the CqROF environment to determine the semi-objective importance of key decision-making criteria. Next, a novel MCDM approach is presented, employing the proposed operators and the ITARA method. A numerical example related to supplier selection is provided to illustrate the application of the proposed approach. Finally, a comparative analysis is conducted, evaluating the designed MCDM approach against various existing approaches.</p>