Remote sensing image scene classification is one of the current hot topics in the field of remote sensing image processing. Since convolutional neural networks （CNNs） have powerful feature extraction capabilities， they have been widely applied in remote sensing image scene classification. However， the current methods have not fully considered and utilized the complementary information between different layers of CNN and the spatial context information of remote sensing images， resulting in that the corresponding classification accuracy needs to be improved. In order to address these issues， a multilayer feature context encoding （MFCE） network is proposed and utilized to solve the problem of scene classification for remote sensing images. The proposed network is composed of two parts： 1） A densely connected backbone； 2） A multiscale context encoding （MCE） module. The former is adopted to fuse the feature information of different layers of CNN， and the latter is utilized to encode and exploit the spatial context information that resides in the multilayer features. Experimental results on two large-scale remote sensing image datasets demonstrate that compared with the existing remote sensing image scene classification methods， the proposed network framework can achieve a significant gain in classification accuracy.