Hyperspectral remote sensing image has dozens or even hundreds of bands. It is a comprehensive carrier of many kinds of information， including rich radiation， spatial and spectral information and is widely used in the field of terrain mapping. However， the traditional hyperspectral image classification methods mostly focus on the feature extraction of spectral dimension， but ignore the features of spatial dimension， which affects the accuracy of classification. The three-dimensional convolutional neural network （3D-CNN） can convolute data in three dimensions at the same time， so this paper uses 3D-CNN depth network to classify ground objects with hyperspectral images， and proposes an improved algorithm based on 3D-CNN for hyperspectral remote sensing land-cover classification. The method can reuse the extracted spatial and spectral features and give full play to the value of features. In addition， this paper introduces the idea of shallow feature preservation network， and proposes a depth network model of image classification integrating shallow feature preservation， which further improves the accuracy of hyperspectral remote sensing land-cover classification. Experimental results of two commonly used hyperspectral remote sensing image data sets （Indian Pines and Pavia University） under the framework of Tensorflow show that compared with the basic 3D-CNN network， the classification accuracy of the proposed method is improved by nearly 2%.