Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model’s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. Code will be available.