Title: Learning Frequency-aware Network for Continual Learning. Abstract: As a challenging problem, continual learning aims to solve the problem that the model does not forget the knowledge of the old model as much as possible when learning new tasks. Most current algorithms perform the same processing on each pixel of the image. As people have different memory abilities for image details and the whole, the forgetting of different parts of the image by the neural network is also asynchronous. In this paper, we discuss the problem of asynchronous forgetting of images at different frequencies. To solve this problem, we propose a solution from the perspective of network structure design and feature preservation. In terms of network structure, we design a dual stream network with high and low frequency separation, and use the characteristics of CNN and transform to process the high-frequency and low-frequency information of images respectively; in the aspect of feature preservation, we design a dynamic distillation loss function to dynamically adjust the preserved weight of high-frequency and low-frequency information according to the training stage of the network. We have verified the effectiveness of our scheme through a series of experiments.