Commonsense video captioning requires the model not only to describe visible content but also to infer multiple types of commonsense captions, including “Intention”, “Effect”, and “Attribute”. Existing methods generally fall into two categories: one extracts commonsense directly from videos but struggles to bridge the semantic gap between visual content and implicit commonsense under limited knowledge; the other leverages language model-extended textual knowledge, which alleviates this gap but overlooks the semantic relationships among different types of commonsense information, limiting reasoning capability. To address these challenges, we propose a Semantic Relation-Guided Network (SRG-Net) for commonsense video captioning. Specifically, a Commonsense Semantic Relation Modeling (CSRM) module is designed to capture interrelations among different types of extended commonsense knowledge and enhance their representations. Furthermore, a Hierarchical Fusion Decoding (HFD) strategy is adopted. Multimodal video features are first fused, followed by the integration of enhanced commonsense representations, enabling the generation of accurate and fluent commonsense captions. Extensive experiments on the large-scale Video-to-Commonsense dataset demonstrate that SRG-Net achieves superior performance compared to existing methods across multiple metrics.