The rapid advancement of cloud computing has enabled the on-demand, pay-as-you-go use of valuable computing resources and has also driven the rapid growth of service-based distributed systems. These systems integrate existing service components in the form of business processes. With the advent of the 5G-Advanced and 6G eras, such cloud-network systems have imposed heightened performance requirements, with ultra-high reliability becoming a critical common indicator for the future development of multimodal cloud computing and communication network systems. Consequently, this paper proposes a reliability enhancement scheme for distributed cloud service systems (DCSS). Initially, the framework employs a reliability model grounded in classical reliability theory to ascertain system reliability. Subsequently, components with a greater impact on system reliability were filtered using an importance assessment algorithm. Ultimately, the allocation of reliability techniques is formulated as an integer nonlinear programming (INLP) problem, aiming to maximize reliability improvements subject to cost constraints, with our proposed redundancy allocation algorithm based on transformer deep reinforcement learning (RAAT-DRL) facilitating optimal decision-making. The experiments demonstrate that, under the same cost constraints, our proposed method achieves a more significant enhancement in system reliability compared to other representative approaches.