<p>Complex evidence theory (CET) plays a critical role in addressing uncertainty within the complex domain. However, accurately measuring conflicts between complex mass functions (CMFs) remains a challenge. To solve this issue, we propose the symmetric complex Renyi (SCR) divergence, which extends the traditional Renyi divergence into the complex domain by incorporating both magnitude and phase information. SCR divergence satisfies the essential properties of symmetry, non-negativity, and non-degeneracy, making it a reliable tool for conflict quantification in uncertain environments. Based on SCR divergence, we develop a novel multi-source information fusion algorithm that dynamically adjusts evidence weights according to conflict levels, effectively mitigating inconsistencies and improving fusion outcomes. Numerical experiments validate the efficiency and robustness of the proposed method, demonstrating its advantages over traditional approaches. Furthermore, the proposed method is applied in medical diagnosis and target recognition, showcasing its practicality and effectiveness in real-world decision-making scenarios. These results highlight the potential of the SCR divergence and the fusion algorithm to address conflict resolution and information integration challenges in complex systems.</p>