This catalyst will encompass two main scenarios: Scenario One, Low-energy-consumption computing network services. As is well known, energy consumption is the ultimate issue in the development of AI technology and business. Creating a low-energy, high-efficiency computing network infrastructure is the foundation for the sustainable development of intelligent business. This scenario will integrate advanced technologies such as digital twins, AI decision-making, and interactions between twins and physical facilities to achieve low-energy computing network services, reducing the computational costs of AI business while ensuring sustainable development. Scenario Two, One-stop computing network business empower SMEs. SMEs in industries such as agriculture and forestry often have resources but lack management, operations, and sales channels, thus failing to make profits. In this scenario, operators will combine network services, public clouds, specialized platforms, AI models, and AIGC applications to provide full-stack services for SMEs, helping customers achieve value transformation and driving business growth for operators. In this scenario, new technologies such as heterogeneous resource scheduling, scale-out intelligent computing center, OTN lossless transmission adapted for AI training scenarios, multi-cloud interconnection, dedicated cloud access, and cross-domain orchestration will be applied.