Addressing the accuracy and stability issues of target tracking in radar network systems, an improved Fuzzy C-Means (FCM) clustering track fusion algorithm is proposed. The method maps local tracks into a fuzzy sample space and introduces sensor measurement quality factors into the objective function. By integrating Interacting Multiple Model (IMM) filtering, information entropy is employed to quantify the uncertainty and reliability of local data, enabling adaptive non-linear weight allocation. Unlike traditional FCM, this approach effectively identifies and suppresses low-quality measurement sources without requiring precise prior noise statistics. Simulation and experimental results demonstrate that the proposed algorithm significantly enhances fusion precision and global stability, particularly during target maneuvers, providing a robust solution for multi-radar cooperative surveillance.