This paper presents an enhanced algorithm for identifying key players in social networks by employing a parallel splitting method. The method optimizes the process of determining influential nodes within large-scale networks by dividing the computational tasks across multiple processors, thereby improving efficiency and accuracy. The proposed approach is tested against existing algorithms and demonstrates superior performance in both computational speed and accuracy of key player identification. The results suggest that this method can be effectively applied to various real-world social network analyses, providing a more scalable and reliable solution.