The existing domain name detection methods for domain generation algorithm (DGA) generally have the characteristics of weak feature extraction ability and high feature information compression ratio, which lead to feature information loss, feature structure destruction, and poor domain name detection performance. Aiming at the above problems, a DGA domain name detection method based on double branch feature extraction and adaptive capsule network is proposed. Firstly, the original samples are reconstructed through sample cleaning and dictionary construction, and the reconstructed sample set is generated. Secondly, the reconstructed samples are processed by a double branch feature extraction network, in which the local features of domain name are extracted by using a sliced pyramid network, the global features of domain name are extracted by using a transformer, and the features at different levels are fused by using lightweight attention. Then, an adaptive capsule network is used to calculate the importance coefficient of the domain name feature map, convert domain name text features into vector domain name features, and calculate the domain name classification probability based on text features by feature transfer. Meanwhile, multilayer perceptron is used to process domain name statistical features to calculate the domain name classification probability based on statistical features. Finally, domain name detection is performed by combining the domain name classification probabilities from two different perspectives. A large number of experiments show that the method proposed in this study achieves leading detection results in DGA domain name detection and DGA domain name family detection and classification, where the F1-score in DGA domain name detection increased by 0.76% to 5.57%, and the F1-score (macro average) in DGA domain name family detection classification increased by 1.79% to 3.68%.