Due to the problem that the feature dimension of convolutional neural network is high and the single-layer feature cannot accurately express the complex semantic information of high-resolution remote sensing image, a scene classification method of the low dimension of convolutional neural network (LDCNN) based on multi-kernel SVM is proposed in this paper. Firstly, the pre-trained convolutional neural network is modified into a low-dimensional network structure. Then, different high-level features extracted from the low-dimensional network is performed to find the corresponding optimal kernel function via SVM classification using different kernel functions, and these multiple optimal kernel functions are fused into a new composite kernel. Finally, multi-kernel SVM classification is carried out. Experimental results show that the proposed method has low feature dimension, and can combine the advantages of features extracted from each layer via multi-kernel SVM, thus achieving more than 99% classification accuracy on two benchmark datasets. In addition, the experiment also proves that this method has strong transfer learning ability.