A scene classification algorithm of remote sensing images based on the integrated convolutional neural network (CNN) is proposed. A back-propagation network is constructed to measure the complexity of scene images. The classification of these images is conducted with the CNN based on the complexity level of each image, thus, the scene classification of remoting sensing images is achieved. With the proposed algorithm, the experimental verification of the open data of NWPU-RESISC45 is conducted and the classification accuracy of 89.33% for Type I test and that of 92.53% for Type II are obtained, respectively. The average running time is 0.41 s. Compared with the VGG-16 model for fine tuning and training, the classification accuracy by the proposed algorithm is increased by 2.19% and 2.17%, respectively. Simultaneously, the prediction rate is increased by 33%. Thus, the efficiency and practicality of this proposed algorithm are confirmed.