The defects of large randomness and few actual samples in the detection of malicious domain names would lead to the overfitting in deep learning model training. A malicious domain name detection method based on group convolutional neural network is proposed. Firstly, the domain name is converted into embedded word vector representation; secondly, a random data set is generated through a combination of random dimensions and convolutional neural network groups are constructed. The Inception structure is added to the network due to its advantages. For the imbalance problem of the inter? class samples, the inter?class balance coefficient is introduced to suppress the model training overfitting and improve the model generalization ability. The experimental results show that the constructed model can effectively detect malicious domain names on the collected domain name detection data set; after parameter optimization, the group convolutional neural network improves the detection accuracy of the constructed domain name detection set by 4% and 1% respectively compared with the shallow model combination classifier and the typical deep neural network model Long Short-Term Memory Convolutional Neural Network(LSTM?CNN), which reaches 98.9%.