Face forgery detection has received considerable attention due to security concerns about abnormal faces generated by face forgery technology. While recent researches have made prominent progress, they still suffer from two limitations: a) the learned features supervised by softmax loss are insufficiently discriminative, since the softmax loss fails to explicitly boost inter-class separability and intra-class compactness; b) hand-crafted features are unable to effectively mine forgery patterns from frequency domain. To address the two problems, this paper proposes a novel frequency-aware discriminative feature learning framework. Specifically, we design an innovative single-center loss which compresses mere intra-class variations of natural faces while encouraging inter-class differences between natural and manipulated faces in the embedding space. Supervised by such a loss, more discriminative features can be learned with less optimization difficulty. As for frequency-related features, a frequency feature adaptively generated module is developed to capture frequency clues in a data-driven manner. Besides, to better fuse the features of both RGBdomain and frequency domain, this paper devises a fusion module based on positional correlation of features. The effectiveness and superiority of our framework have been proved by extensive experiments and our approach achieves state-of-the-art performance in both in-dataset and cross-dataset evaluation