ing to address the low identification accuracy of remote-sensing tree species of forests with a complex canopy and high density, a three-dimensional convolution neural network (3D-CNN) that can extract the stereoscopic features of hyper-dimensional data is introduced herein, and it can identify remote-sensing images. Furthermore, it is improved through residual network (ResNet) to build a 3D residual convolution neural network (3D-RCNN) to reduce the influence of degradation phenomenon and the inaccuracy caused by network depth. The sample set is constructed by combining GF-5 hyperspectral data (GF-5 AHIS)and GF-6 high spatial resolution data (GF-6 PMS), supplemented by forest resource data and field survey data. Then, a tree species recognition model is constructed based on the concept of 3D-RCNN. The experimental results show that compared with traditional 3D-CNN, the proposed 3D-RCNN increases the model network's density from 12 layers to 18 layers, which can deepen the network structure and alleviate network degradation. By combining GF-5 AHIS and GF-6 PMS, 3D-RCNN can effectively identify northern subtropical forest species, providing better recognition accuracy (91.72%) than traditional 3D-CNN (85.65%) and support vector machine algorithm (85.22%).