Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow classification technology, software-defined networking (SDN), and programmable network devices have enabled the network to identify user demands and precisely control fine-grained traffic routing quickly. Although significant progress has been made with these technologies, optimizing the transmission paths of business flows to meet diverse QoS requirements in real-time remains a challenge. To address this issue, this paper proposes a dual-channel fusion routing algorithm based on multi-agent reinforcement learning (MARL-DCF). Implemented within an SDN architecture, the algorithm was experimentally validated in a Mininet environment using real network topologies and traffic trajectories. The results demonstrate that MARL-DCF can optimize routing paths according to the network environment, meet various QoS demands, and exhibit strong adaptability and reliability in complex scenarios such as link failures and traffic fluctuations.