###### Abstract
[16; 17] achieved the goal of extracting triplets in one shot. Specifically, [PERSON] et al. [16] proposed a position-aware annotation strategy aimed at overcoming the limitations of the existing works by enhancing the expressiveness of labels. [PERSON] et al. [17] proposed a scheme that achieved end-to-end task processing by tagging relationships between all word pairs. Despite the advantages of end-to-end methods and grid tagging schemes, it should be noted that end-to-end methods may be limited by their high demand for a large amount of labeled data and excessive reliance on data distribution, while grid tagging schemes may fail to handle highly complex or ambiguous text relationships. Due to the complexity of language structure, accurate sentiment analysis often requires a comprehensive consideration of contextual information, syntactic features, and other relevant information. Graph neural network (GNN), neural networks tailored for graph-structured data, have garnered significant interest for their distinct benefits. Subsequently, graph convolutional neural network (GCN) and graph attention network (GAT) based on GNN have been introduced into sentiment analysis tasks to enhance the understanding of model and utilization of syntactic structure. For example, [PERSON] et al. [18] constructed directed graphs to integrate syntactic relationships, and GCNs were used to improve the accuracy of machine translation models, demonstrating their powerful ability to capture syntactic features in text. A common approach to more finely pair aspects with their corresponding opinion representations is to use syntactic dependency trees to construct adjacency matrices for sentence graph structures. However, there are still shortcomings in the analysis that solely relies on the adjacency matrix generated by the dependency tree. To highlight highly relevant information in the dependency tree, [PERSON] et al. (2019); [PERSON] et al. (2020), and [PERSON] et al. (2021) introduced attention mechanisms to construct new edges in the graph structure, thereby enhancing the ability of the model to grasp dependency relationships. [PERSON] et al. (2020) and [PERSON] et al. (2020) used GAT on graph-structured data, where words in sentences are nodes in the graph and grammatical relationships are edge labels. The dependency graphs used in the model are obtained from ordinary dependency tree parsers or modified using heuristic rules. Meanwhile, the widespread use of GCN in processing dependency graphs and the effectiveness of GAT in handling aspect triplet extraction tasks demonstrate the potential of these two graph neural networks in enhancing feature representation. Specifically, [PERSON] et al. (2020) used GAT to focus on sentiment words that are more closely related to aspect terms through target-dependent methods, which helps to address syntactic ambiguity and clearly establish dependencies between words. However, the above studies only used a single graph neural network and did not take full advantage of graph neural networks. Therefore, considering the respective advantages of GCN and GAT in processing graph-structured data and enhancing syntactic and semantic relationships, this study aims to fuse GCN and GAT to further improve the performance and accuracy of aspect sentiment triplet extraction tasks by integrating the strengths of these two graph networks, overcoming the limitations of existing methods, and effectively capturing and representing complex text relationships. In this paper, a new end-to-end model is proposed, which integrates graph convolutional networks and graph attention networks with an improved biaffine attention mechanism for the task of aspect sentiment triplet extraction. The main contributions of this study are summarized as follows:  
* An innovative end-to-end solution that enhances the flexibility and portability of a model through modular design, capable of effectively handling multiple subtasks within a single end-to-end process, significantly improving the processing efficiency and accuracy.