Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.