Abstract: Cloud manufacturing, as a collaborative paradigm that enhances supply chain resilience and enterprise competitiveness through resource sharing and service integration, faces significant challenges. Existing cloud manufacturing platforms rely heavily on historical data for task matching, struggling to dynamically adapt to real-time changes in enterprise status and service demands. Additionally, the lack of efficient process visualization and monitoring methods hinders timely issue detection and resolution during service execution. Furthermore, insufficient capability prediction and optimization limit resource scheduling and service quality assurance, failing to meet user requirements for high reliability and flexibility. This study proposes a digital twin-driven cloud manufacturing service platform framework, elaborates its operational mechanism, and focuses on three key technologies: digital twin modeling, model validation, and dynamic visualization monitoring in cloud manufacturing environments. The research aims to provide theoretical foundations and technical support for improving the efficiency and quality of cloud manufacturing services.