The extraction of image feature points and descriptors is the foundation of some tasks such as SLAM, SFM, and 3D reconstruction. Preeminent algorithms for image feature point and descriptor extraction play a significant role in processing these tasks. This study accomplishes some improvements in the SuperPoint network with high robustness and good performance in the extraction of feature points and descriptors. Considering the flaws of the heavy calculation burden and massive parameters, the authors first change the ordinary convolution to depthwise separable convolution, the number of layers, and the down-sampling method. Afterward, the channel pruning algorithm is perfected so that it can be applied to depthwise separable convolution and prune the network. Experiments have proved that this study reduces the network parameter number and calculation burden respectively to 15% and 5% those of the original SuperPoint network, and the FPS is increased by 6.64 times under the condition of a slight loss of feature point detection and matching effects. Thus, good experimental results are achieved.