The task of network alignment is to obtain the node pairs which belong to the same entity from different networks. Existing embedding-based network alignment methods either use node structural or attribute information as inputs for node embeddings. These pieces of information are not always available in real-world datasets, and current methods that consider single information embedding may fail when there is excessive network noise. To address the aforementioned issue, we utilize a multi-layer Graph Attention Networks(GATs) to design an unsupervised node embedding model, which trains two GATs for structural and attribute information in a single graph and embeds the source nodes and target nodes into the same embedding space. By applying graph augmentation techniques, the model learns structural embeddings and attribute embeddings for every node in the networks based on structural and attribute consistency. Moreover, we apply a topological alignment refinement process to get aligned node pairs, which further enhances the accuracy of network alignment by leveraging the similarity of the structure between networks. Through extensive experiments, we have demonstrated that our model outperforms the state-of-the-art models in terms of alignment accuracy and its ability to handle attribute and structural noise. Additionally, our model exhibits relatively low complexity.