To enable intelligent and low-cost End-to-End (E2E) network service deployment and Service Level Agreement (SLA) quality management in the two-level Cloud Radio Access Network (C-RAN), this paper studies a DTN-based SLA quality closed-loop management scheme, which mainly includes acquisition module, base module, deployment module, and monitoring module. The deployment module is responsible for constructing the service deployment optimization model with the goal of minimizing the average E2E delay of packets, and quickly obtain deployment decisions through a Weighted GraphSAGE (WGraphSAGE)-assisted Double Deep Q-network (DDQN)-based two-stage service deployment (WDTSD) algorithm. The monitoring module uses the state monitoring model based on Bayesian Convolutional Neural Network (BCNN) to complete the abnormal detection of physical devices. The modular closed-loop interaction provides a virtual environment for network service deployment, verification, monitoring, and policy revision, achieving SLA quality assurance. Extensive results validate the effectiveness of the WDTSD algorithm, state monitoring model, and DTN. WDTSD outperforms existing solutions in terms of memory overhead, computing speed, E2E delay, and service access ratio. The state monitoring model has better performance in indicators such as accuracy. The results under different data acquisition periods show that the service deployment effect is better when the DTN is closer to the physical network.