The goal of few-shot fine-grained image classification (FSFGIC) is to distinguish subordinate-level categories with subtle visual differences such as the species of bird and models of car with only a few samples. In this work, we argue that a designed network that has the ability to better distinguish feature descriptors of different categories will effectively improve the performance of FSFGIC. We propose a re-abstraction and perturbing support pair network (RaPSPNet) for FSFGIC. Specifically, we first design a feature re-abstraction embedding (FRaE) module which can not only effectively amplify the difference between the feature information from different categories but also better extract the feature information from images. Furthermore, a novel perturbing support pair (PSP) based similarity measure module is designed which evaluates the relationships of feature information among a query image and two different categories of support images (a support pair) at the same time for guiding the designed FRaE module to find salient feature information from the same category of query and support images and find distinguishable feature information from the different categories of query and support images. Extensive experiments on FSFGIC tasks demonstrate the superiority of the proposed methods over state-of-the-art benchmarks.