Knowledge graphs (KGs) serve as critical structured representations of the real world, modeling entities and their complex relational patterns. While traditional knowledge graph embedding (KGE) methods excel at capturing structural regularities through geometric or tensor operations, they have limitations in effectively integrating structural and textual information. To address this limitation, we propose TSKE, a novel dual-branch architecture that effectively integrates both textual and structural information. The text branch employs language models to encode textual descriptions, generating context-aware semantic representations. Simultaneously, the structure branch introduces a multi-scale dilated attention mechanism that captures local neighborhood patterns and global graph dependencies through hierarchical receptive fields. The efficient channel attention further enhances feature selectivity by adaptively filtering noise and emphasizing feature relevance. TSKE incorporates an adaptive fusion module that dynamically adjusts the contribution weights of textual and structural features. Experimental results demonstrate TSKE’s superior performance in KGE.