In recent years, with the rise of various online learning plat-forms and game live broadcasting industry, screen content video is explosively increasing. There is an urgent demand to reduce the inevitable compression artifacts produced by traditional lossy compression method. However, there do not exist any research on compressed Screen Content Video (SCV) quality enhancement. Since SCV frames always con-sist of two main types of contents with different characteris-tics, i.e., text and graphic, a Content Adaptive model based on Two branches (CAT) is proposed in this paper. For en-hancing graphic, we utilize temporal information by motion compensation, while for enhancing text, we explore spatial relevance in horizontal and vertical directions to recover sharp edges. Furthermore, a content adaptive block is used to select content related features for the collaborative enhancement of both contents. We build a large SCV dataset compressed by H.266/VVC Test Model (VTM 12.1). On this dataset, experi-mental results demonstrate that the proposed method achieves the state-of-the-art performance on 10 different kinds of SCV test sequences.