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This paper conducts an in-depth study on the outage probability performance of relay-based semantic communication systems and proposes a multi-mode intelligent relay design framework to address complex scenarios such as background knowledge differences, channel quality fluctuations, and computational limitations at the destination node. Based on a three-node two-hop communication model (source node–relay node–destination node) and integrating the DeepSC model, the study achieves cross-layer collaboration between semantic encoding/decoding and channel encoding/decoding. The proposed relay node operates in four innovative modes: semantic cooperative decode-and-forward, semantic adaptive forwarding, semantic-enhanced forwarding, and semantic-bit hybrid forwarding, each tailored to different levels of background knowledge matching, channel conditions, and computational constraints at the destination node. Through theoretical derivations, this paper presents the first closed-form expressions for the outage probability of the four relay modes, systematically quantifying the coupling effects of semantic symbol redundancy, background knowledge differences, and computational conversion efficiency on system reliability. The results show that semantic adaptive forwarding significantly reduces outage probability when background knowledge differences are minimal. When the destination node has limited computational power, the semantic-bit hybrid mode enhances communication reliability by flexibly adjusting the transmission strategy. Moreover, proper configuration of semantic symbol redundancy plays a crucial role in maintaining semantic information integrity and resisting channel interference. Monte Carlo simulations validate the theoretical analysis, demonstrating that the dynamic switching mechanism of the multi-mode relay outperforms single-mode strategies. This research provides theoretical support for reliable transmission and resource optimization in 6G semantic communication systems, uncovering the potential of joint optimization between semantic parameters and dynamic channel conditions. It holds significant implications for advancing future intelligent communication systems.
