With the rapid development of computer vision, point clouds technique was widely used in practical applications, such as obstacle detection, roadside detection, smart city construction, etc. However, how to efficiently identify the large scale point clouds is still an open challenge. For relieving the large computation consumption and low accuracy problem in point cloud classification, a large scale point cloud classification framework based on light bottle transformer (light-BotNet) is proposed. Firstly, the two-dimensional (2D) and three-dimensional (3D) feature values of large scale point cloud were extracted for constructing point cloud feature images, which employed the prior knowledge to normalize the point cloud features. Then, the feature images are input to the classification network, and the light-BotNet network is applied for point cloud classification. It is an interesting attempt to combine the traditional image features with the transformer network. For proving the performance of the proposed method, the large scale point cloud benchmark Oakland 3D is utilized. In the experiments, the proposed method achieved 98.1% accuracy on the Oakland 3D dataset. Compared with the other methods, it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.