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Networks have been widely used as data structures for abstracting real-world systems and for organizing relationships between entities. The network embedding model is a powerful tool to map the nodes in the network into a continuous vector space representation. The network embedding method based on Graph convolutional neural (GCN) is easily affected by the random optimization of parameters in the model iteration process and the aggregation function. The problem of loss of original node feature information. In order to effectively improve the network embedding effect, a graph attention network based on the second-order neighborhood cardinality retention strategy is proposed for the limitation of the graph neural network model in the node representation learning in the network embedding. (SNCR-GAT, Second-order Neighborhood Cardinality Retention strategy Graph attention network), by aggregating the second-order neighborhood feature cardinality, it solves the problem of important information retention in the process of latent feature learning of network nodes; by classifying and visualizing two networks in nodes Experiments are carried out on the actual task of embedding, and the results show that the performance of the SNCR-GAT model on network embedding is more superior than the baseline method.