###### Abstract  
The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure information to establish the connection between aspects and opinion words. However, the accuracy of these methods is limited by noise information and dependency tree parsing performance. To solve this problem, we proposed an attention-enhanced graph convolutional network (AEGCN) for aspect-based sentiment classification with multi-head attention (MHA). Our proposed method can better combine semantic and syntactic information by introducing MHA and GCN. We also added an attention mechanism to GCN to enhance its performance. In order to verify the effectiveness of our proposed method, we conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can make more reasonable use of semantic and syntactic information, and further improve the performance of GCN. aspect-based sentiment classification; attention mechanism; multi-head attention; graph convolutional network +
Footnote †: journal:  
based on the syntactic structure of the sentence, use the dependency tree, and Graph Convolutional Networks (GCN) to update the representation of the sentence [11; 12; 13; 14]. There is no doubt that dependency trees can establish long-term dependencies between aspects and opinion words. However, due to the limitations of the dependency tree itself, when the sentence structure is more complex or the sentence expression is more colloquial, the model usually cannot correctly predict the sentiment polarity of the aspect. Specifically, when the dependency tree cannot correctly establish the connection between the aspect and the opinion word, the model will not be able to accurately predict the sentiment polarity of the aspect. In addition, in the process of using the dependency tree and GCN to update the word representation, noise information is usually integrated into the new representation, causing the model to learn wrong parameters. To solve the above two problems, we proposed an attention-enhanced graph convolutional network for aspect-based sentiment classification with multi-head attention. For the first problem, we combine the graph convolutional network with the multi-head attention, using the advantages of the multi-head attention mechanism to capture contextual semantic information to alleviate the defects of the graph convolution network in processing data with unobvious syntactic features. For the second problem, we introduce the attention mechanism into the traditional graph convolutional network, alleviating the problem of introducing too much noise when updating node information by assigning appropriate attention weights to each adjacent node. In this paper, we divide multi-head attention into multi-head self-attention (MHSA) and multi-head interactive attention (MHIA). The model is divided into two parts, and both parts use the same input. The first part captures contextual semantic information though the attention coding layer (ACL), and the second part integrates syntactic information through the attention-enhanced graph convolutional network. Finally, we use multi-head interactive attention (MHIA) to integrate the above information to obtain the final feature representation. We conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can effectively utilize syntactic and semantic information, and further improve the performance of graph convolutional networks. Our contributions are as follows:  
1. We introduced an attention mechanism into the graph convolutional network to enhance its performance. 2. We introduced multi-head self-attention to capture contextual semantic information, used multi-head interactive attention to interact semantic and syntactic information to obtain a more complete feature representation. 3.