In remote sensing image scene classification,a classification algorithm based on convolutional neural network (CNN) has the dependence on training data,and the classification effect is poor in the absence of training data,and a classification algorithm based on transfer learning is proposed.Firstly,the existing pre-training model of multiple CNN is selected,and the model is fine-tuned by using the advantages of transfer learning to extract the different high-level features of the image,then,the fusion of the image′s many high-level features makes the feature information more abundant,and finally,the merged high-level features are input into the remote sensing image classifier based on logical regression,and the classification results of remote sensing images are obtained.Experiments are carried out in remote sensing data sets of UCMerced_LandUse,and the existing algorithms are compared and analyzed,and the proposed algorithms are significantly improved in three evaluation indicators.By analyzing the experimental results,it is shown that the algorithm can achieve 92.01% classification accuracy and 91.61% Kappa coefficient under only 10% of the training data.