Nowadays, deep learning is widely used to detect interest points and extract the corresponding descriptors and achieved suitable results for many applications of computer vision, such as image matching, three-dimensional reconstruction, simultaneous localization, and mapping. We propose an approach for interest point detection and descriptor extraction using pyramid convolution and circle loss, which is named as PC-SuperPoint. We utilize pyramid convolutions in the backbone network, which includes convolution kernels of different scales for multiscale feature extraction. The following well-designed networks are able to capture the local and global information from the obtained backbone feature maps. In addition, circle loss, which enhances weight attributes for each pair of descriptors, is also applied to improve the convergence speed in the training phase. Experiments on the HPatches dataset and KITTI dataset achieve promising results, which reveal the effectiveness of the proposed method.