Network alignment aims at detecting the corresponding entities across multiple networks, which is an essential basis for the fusion and analysis of multiple network information. Moreover, embedding-based network alignment has gradually become one of the promising methods. However, existing methods ignore the confusing selection problem caused by the similarity-orientated principle of network embedding and over-dependence on the hypothesis of structural consistency. In this paper, we propose an end-to-end Graph Alignment Neural Network (GANN) model with graph-to-sequence learning. GANN mainly consists of two modules: Graph encoder and Sequence decoder. In graph encoder module, we present a restricted network embedding method, which can not only capture the local structure and attribute information of nodes but also realize the constraint of node embedding and space reconciliation. In sequence decoder module, we propose a graph-to-sequence learning model to address large graphs' structural consistency hypothesis problem. In this model, an attention-based LSTM mechanism is introduced to infer a node in the source network corresponding to the candidate node sequence in target networks. In this candidate sequence, the correct aligned node is placed at the top. We demonstrate that GANN outperforms the state-of-the-art methods in network alignment tasks on various real-world datasets.