A 3D point cloud super-resolution network with dynamic residual graph convolution (PSR-DRGCN) was proposed to efficiently extract of local information from 3D point clouds of non-European data in super-resolution. The network includes feature extraction module, DRGCN module and upsampling module. For the input point cloud, the feature extraction module locates k nearest points of each point in 3D space by k-NN algorithm and then converts the local geometry information into the high dimensional feature space through a multi-layer pointwise convolution. The DRGCN module converts the local geometry feature of each point into the semantic feature through a multi-layer graph convolution. It dynamically adjusts the neighbor space of the point in each layer to increase the receptive field range and effectively fuse the semantic information of different levels through residual connection, which makes the extraction of local geometric information efficient. The upsampling module adds the number of points and maps them from feature space to 3D space. The results showed that at 2× magnification of the high-resolution point cloud generated by PSR-DRGCN, the similarity indexes CD, EMD and F-score compared with the second network were increased by 10.00%, 4.76% and 16.84% respectively. Compared with the second network, the similarity indexes at 6× magnification were increased by 2.35%, 40.00% and 0.58% respectively. In all cases, the optimal effect was achieved on the mean and the std indicators and the generated high-resolution point cloud quality was high.