An adaptive optimization method for reliable encapsulation of manufacturing service based on a graph convolution network with multi-dimensional feature fusion

In the context of mass customization, manufacturing services require frequent adjustment and configuration of resources. Enterprises lack a unified model to describe the reliability of manufacturing service encapsulation under complex resource combinations. Meanwhile, during the encapsulation process, the latent relationships between heterogeneous resources are challenging to extract, making it difficult to guarantee the reliability of manufacturing services, ultimately resulting in production efficiency and product quality failing to meet customer demands. To address the issues above, this paper proposes A novel adaptive optimization method for reliable encapsulation of manufacturing service based on a graph convolution network with multi-dimensional feature fusion. First, a novel adaptive optimization method for manufacturing service reliability encapsulation (AO-MSRE) is constructed to characterize encapsulation reliability under diverse resource combinations. Subsequently, based on the characteristics of this model, the graph convolutional network (GCN) algorithm was improved, and the improved GCN algorithm was combined with the edge graph neural network (EGNN). A novel multi-dimensional feature fusion graph convolutional network (MFFGCN) framework—specifically, the reliability characterization network (RCN)—was designed. This framework integrates node, edge, and edge-graph feature information to comprehensively analyze the impacts of heterogeneous resources, resource relationships, and implicit interference between relationships on encapsulation reliability. Experimental results demonstrate that RCN achieves outstanding performance in optimizing manufacturing service reliability encapsulation, with a mean absolute percentage error (MAPE) as low as 1.95% while maintaining robustness under noise interference. This work provides a theoretical foundation and practical tools for reliable manufacturing service encapsulation in dynamic production environments.