With the rapid development of the Internet and the growing complexity of the network topology, network anomaly has become more diverse. In this paper, we propose an algorithm named Deep Adaptive Feature Learning (DAFL) for traffic anomaly detection based on deep learning model. By setting proper feature parameters $$\theta $$ on the neural network structure, DAFL can effectively generate low-dimensional new abstract features. Experimental results show the DAFL algorithm has good adaptability and robustness, which can effectively improve the detection accuracy and significantly reduce the detection time.