With the rapid advancement of technologies such as service computing, cloud computing, IoT, and blockchain, enterprises and organizations have progressively built service ecosystems composed of intelligent entities, including humans, robots, and integrated systems. These systems exhibit collective intelligence and self-organization, evolving from efficiency-driven models to complex systems with nonlinear characteristics. However, translating individual contributions into measurable system outcomes remains a significant challenge. The core contribution of this paper lies in proposing a cyclic “measurement, analysis, and optimization” framework, providing both theoretical and practical support for ecosystem governance, and concluding with future research directions.