Abstract: In industrial internet environments, industrial software systems are usually large-scale and with a complex and heterogeneous array of software systems and physical devices. Based on the unified service-oriented and component-based encapsulation of heterogeneous devices and software objects, diverse applications of industrial software systems can be flexibly constructed through the orchestration of service components. However, it usually requires high efficiency of service orchestration and the assurance of service quality. To enhance the efficiency of service component orchestration and ensure service quality in industrial internet environments, this paper proposes a service component orchestration method called FGraphSAGE_GA, which formalizes the convert the component orchestration problem in industrial internet environments into a link prediction problem within a graph structure. It employs a supervised learning approach to train the prediction model. For the new service component orchestration problem, the trained model performs probabilistic predictions to narrow down the candidate space, followed by the optimization of the service component orchestration plan using a genetic algorithm. Experiments were conducted on two different datasets for service component orchestration problems of three different scales, and experimental results show that the proposed FGraphSAGE_GA algorithm has good performance in terms of service quality and orchestration efficiency.