Domain Generation Algorithm (DGA) algorithm is an important part of C&C attacks. Attackers use DGA domains to establish communication between infected machines and C&C servers, and then manipulate user hosts to achieve malicious attacks. The DGA domain name detection model based on Long Short-Term Neural Network (LSTM) and Transformer has the disadvantages of low accuracy and poor adaptability. In order to solve these problems, this paper proposes a D-mamba model based on the latest Mamba structure, which improves the detection ability of the model through a four-channel feature extraction algorithm. The experimental results show that the D-mamba model achieves 98.97% F1 score for the 2-classification task on the OSINT public dataset, and the D-mamba model achieves 68.75% F1 score for the multi-classification task on the OSINT public dataset, which are higher than other models.