ing at the problems of difficult feature extraction, poor classification accuracy, and less classification types in the remote sensing image multi-classification by the conventional methods, the feasibility of the convolutional neural network (CNN) model and the recognition effects of different CNN models are studied in the multi-classification recognition of hyperspectral remote sensing objects. The datasets are collected from Vaihingen provided by the international society for photogrammetry and remote sensing (ISPRS) and Google Earth. After the dataset-I containing six categories of ground objects is made, the dataset-II and dataset-III are made by adding ten and fourteen categories of ground objects, respectively. Through pre-processing image data, setting up network structures, adjusting model parameters, comparing network models, and so on, the classification accuracies of the above three datasets are all above 95%. By analyzing the influences of different CNN models on the multi-classification recognition of hyperspectral remote sensing objects, the feasibility and high recognition ability of CNN model in the multi-classification recognition of hyperspectral remote sensing are confirmed. The experimental results provide a certain reference for the application of CNN model in the multi-classification recognition of hyperspectral remote sensing objects.