Aiming at the problem of insufficient local and global feature extraction in the feature extraction process of point cloud classification， a point cloud classification algorithm with local-global interaction and structural Transformer was proposed. Firstly， a dual-branch parallel local-global interaction framework was proposed and used to extract local and global features respectively， where in one branch， maximum pooling and convolution were used to extract local features， and in the other branch， global features were extracted by using average pooling and Transformer. Meanwhile， considering the importance of position information in Transformer， a structural Transformer was proposed to further enhance the global structural features by applying interaction of position information with current features for several times. Finally， the local-global features were used for classification to complete the classification task of point cloud. Experimental results show that the classification Overall Accuracies （OAs） of the proposed algorithm are 93.6% and 87.5% respectively on ModelNet40 and ScanObjectNN benchmark datasets. It can be seen that the proposed local-global interaction and structural Transformer network achieve good performance in point cloud classification task.