In Natural Language Processing （NLP） tasks， Aspect Sentiment Triplet Extraction （ASTE） aims to identify the relationships among aspect terms， opinion terms， and sentiment polarity in text， serving as a crucial step in realizing fine-grained sentiment analysis. In current mainstream methods， end-to-end models generally suffer from insufficient understanding of linguistic features and poor handling of the sparsity in sentiment expressions， which limits models’ accuracy and robustness. At the same time， pipeline models are prone to error propagation. To address these issues， an ASTE model with Multi-View Linguistic Features and Sentiment Lexicon （MVLF-SL） was proposed. In this model， multi-view linguistic features were utilized to enhance the model’s ability to understand context and implicit semantics， while additional prior knowledge of sentiment was provided by a sentiment lexicon. Firstly， Graph Convolutional Network （GCN） was used to represent multi-view linguistic features and obtain enhanced linguistic features. Secondly， a dynamic fusion strategy was adopted to integrate the enhanced linguistic features with the sentiment lexicon. Thirdly， multi-layer GCN was employed to enhance the feature representations of aspect and opinion terms by incorporating adjacency relations and node features. Finally， a Boundary-Driven Table-Filling （BDTF） method， improved with a Biaffine Attention （BA） mechanism， was used for decoding and extracting the triplets. Experimental results on four subsets （14res， 14lap， 15res， and 16res） of the ASTE-DATA-V2 dataset show that compared with the BDTF model， MVLF-SL has the F1-scores improved by 0.57， 2.08， 2.20， and 1.74 percentage points， respectively. It can be seen that the proposed model achieves better performance in ASTE， and fully utilizes linguistic features and external sentiment knowledge.