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In intelligent manufacturing for complex products, the configuration and allocation of human-robot collaboration units (HRCUs) are of critical importance for enhancing production performance. To address the insufficient research on the impact of individual irrational behaviors and group-reference behaviors in HRCUs construction, a stable one-to-many human-robot-position matching decision-making (HRPMDM) method in hesitant fuzzy environments is proposed. Specifically, linguistic hesitant fuzzy sets (LHFSs) are adopted to characterize evaluators’ dual uncertainties in linguistic term selection and membership degree assignment. Subsequently, the Cloud Model is adopted to quantitatively transform the LHFSs, thereby providing support for the proposed clustering algorithm based on cognitive similarity, enabling it to divide the matching objects into several subgroups according to the degree of cognitive similarity among individuals. Furthermore, to reduce the bias in attribute weight assessment caused by peer effects, a social network-based algorithm that enables precise quantification of subgroup and member weights is proposed. These quantified weights are then integrated into the group consensus adjustment process to provide reliable reference correction values for individual assessments. Additionally, multi-proposition belief structures are introduced to represent uncertain matching preference rankings (UMPRs) influenced by group reference behaviors, and a corresponding satisfaction measurement method is further developed. Finally, a practical case study demonstrates the operational feasibility and performance efficacy of the proposed method. This study is the first to integrate carbon neutrality cost optimization objectives into human-robot matching decisions and develops a Quality of Service (QoS)-optimized allocation strategy for HRCUs in heterogeneous production environments. The results demonstrate that the proposed matching method has led to significant improvements in both production efficiency and environmental sustainability for complex product manufacturing.