Zero-shot knowledge graph relation prediction aims to infer triplets with unseen relations using the existing fact triplets. The existing methods often underutilize entity descriptions， only considering text information and graph structure information of relations. Text information for relations only relies on dataset-provided text descriptions， so it is difficult to capture the detailed semantics of relations and the overall semantic information of knowledge graph triplets. To address this issue， a zero-shot knowledge graph relation prediction method via dual-channel embedding fusion was proposed. First， a structural encoder and a semantic encoder were used to generate structural and semantic features of triplet entities. Then， the obtained features were fused via a dual-channel entity multimodal information fusion module， and the text generation ability of Large Language Models （LLMs） was used to generate description information after enhancing relation text semantics. Third， through training， semantically enhanced relation features and multimodal entity features were aligned semantically in vector space. Finally， for zero-shot relations， zero-shot knowledge graph relation prediction was realized according to semantically enhanced text description information. Experimental results on two real datasets show that compared to the optimal baseline method， on the NELL_ZS dataset， the proposed method improves Mean Reciprocal Rank （MRR）， Hit@1， Hit@5， and Hit@10 by 8.7， 7.2， 9.5， and 11.2 percentage points， respectively； on the WN18RR_ZSL dataset， the proposed method improves MRR， Hit@1， Hit@5， and Hit@10 by 6.6， 9.2， 3.9， and 0.8 percentage points， respectively， demonstrating the effectiveness of the proposed method.