In the field of cyber security, the mendacious domains generated by the domain generation algorithm (DGA) are called DGA domains. Similar to real domains, they are usually a random combination of characters or numbers, which makes DGA domains highly camouflaged. Hackers take advantage of the disguised nature of DGA domains to carry out cyber attacks, so as to bypass security detection. How to effectively detect DGA domains has become a research hotspot. Traditional statistical machine learning detection methods require the manual construction of domain feature sets. However, the quality of domain features constructed manually or semi-automatically varies, which affects the accuracy of detection. In view of the powerful automatic feature extraction and representation capability of deep neural networks, a DGA domain detection method based on multi-view contrastive learning (MCL4DGA) is proposed. Different from existing methods, it incorporates attentional neural networks, convolutional neural networks, and recurrent neural networks to effectively capture global, local, and bidirectional multi-view feature dependencies of domain sequences. Besides, the self-supervision signals derived by contrastive learning can enhance the expressiveness between multi-view feature learning encoders and thus improve the accuracy of detection. The effectiveness of the proposed method is verified by experimental comparison with current methods on a real dataset.