With the development of SOA technology, Web service is widely used and the number of services is growing rapidly. It is very important to classify Web service correctly and efficiently to improve the quality of service discovery and promote the efficiency of service composition. However, the existing Web service classification technologies have some problems, such as sparse description text, insufficient consideration of attribute information, and structural relationship. Therefore, it is difficult to effectively improve the accuracy of Web service classification. In order to solve this problem, this study proposes a GAT2VEC-based Web service classification method. Firstly, according to the structural relationship between Web services and their own attribute information, several corresponding structural diagrams and attribute bipartite diagrams are constructed respectively, and the random walk algorithm is used to generate the structural context and attribute context of Web services. Then, the SkipGram model is used to train the joint context to obtain the word vector which merges the multidimensional information. Finally, the SVM model is used to perform the classification and prediction of Web services. The experimental results show that compared with the five methods of Doc2vec, LDA, Deepwalk, Node2Vec, and TriDNR, the proposed method has 135.3%, 60.3%, 12.4%, 10.5%, and 4.3% improvement in Macro F1 value, which effectively improves the accuracy of service classification.