Aspect-level sentiment analysis aims to predict the sentiment polarity of specific target in given text. Aiming at the problem of ignoring the syntactic relationship between aspect words and context and reducing the attention difference caused by average pooling， an aspect-level sentiment analysis model based on Alternating-Attention （AA） mechanism and Graph Convolutional Network （AA-GCN） was proposed. Firstly， the Bidirectional Long Short-Term Memory （Bi-LSTM） network was used to semantically model context and aspect words. Secondly， the GCN based on syntactic dependency tree was used to learn location information and dependencies， and the AA mechanism was used for multi-level interactive learning to adaptively adjust the attention to the target word. Finally， the final classification basis was obtained by splicing the corrected aspect features and context features. Compared with the Target-Dependent Graph Attention Network （TD-GAT）， the accuracies of the proposed model on four public datasets increased by 1.13%-2.67%， and the F1 values on five public datasets increased by 0.98%-4.89%， indicating the effectiveness of using syntactic relationships and increasing keyword attention.