Convolutional neural networks have achieved significant success in in signal processing and computer vision, but its underlying mechanisms are not well understood. Recently, the understanding of convolutional neural networks has received more and more attention. The wavelet scattering transform is the pioneering work presented by Mallat who is one the founders of wavelet analysis. It can be proved that it has the properties of translation invariance and deformation stability. In this talk, we will introduce our proposed shearlet scattering transform. It combines the advantages of scattering transform and shearlet. In addition, we construct a hybrid shearlet scattering network by fusing the shearlet scattering transform with an appropriate convolutional neural network, and apply it to COVID-19 detection and fake news detection tasks, both of which achieve good application performance.