<p>The site selection of solar power plants in the digital economy increasingly depends on heterogeneous, uncertain, and credibility-dependent data, yet existing fuzzy correlation measures remain inadequate. Most traditional fuzzy, interval-valued, and credibility-based correlation coefficients fail to simultaneously capture asymmetric uncertainty, credibility-weighted dispersion, and behavioral preferences of experts. To address this gap, this study develops a new class of fuzzy credibility correlation coefficients (FCCCs) based on redesigned scoring functions for fuzzy credibility numbers (FCNs). The proposed FCCCs provide a more discriminative and robustness-enhanced measure of similarity under credibility-driven ambiguity. Furthermore, the FCCC model is integrated with a behavioral TOPSIS (BTOPSIS) framework to incorporate experts’ gain–loss attitudes and decision asymmetry. This yields a comprehensive multi-attribute group decision-making approach capable of modeling both credibility-dependent uncertainty and behavioral risk preferences. A real-world solar-site selection problem in the digital-economy context is used to validate the method. Results show that FCCC–BTOPSIS produces more stable rankings across parametric variations, achieves stronger sensitivity robustness, and demonstrates clearer differentiation among alternatives compared with two widely used fuzzy/credibility correlation models. Overall, the study contributes a theoretically grounded and behaviorally enriched decision-making framework with improved accuracy, stability, and interpretability. The findings offer methodological advances for renewable-energy planning and provide practical decision support for solar infrastructure deployment under the digital economy.</p>