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DGA-based botnet, which uses Domain Generation Algorithms (DGAs) to evade supervision, has become a part of the most destructive threats to network security. Over the past decades, a wealth of defense mechanisms focusing on domain features have emerged to address the problem. Nonetheless, DGA detection remains a daunting and challenging task due to the big data nature of Internet traffic and the potential fact that the linguistic features extracted only from the domain names are insufficient and the enemies could easily forge them to disturb detection. In this paper, we propose a novel DGA detection system which employs an incremental word-embeddings method to capture the interactions between end hosts and domains, characterize time-series patterns of DNS queries for each IP address and therefore explore temporal similarities between domains. We carefully modify the Word2Vec algorithm and leverage it to automatically learn dynamic and discriminative feature representations for over 1.9 million domains, and develop an simple classifier for distinguishing malicious domains from the benign. Given the ability to identify temporal patterns of domains and update models incrementally, the proposed scheme makes the progress towards adapting to the changing and evolving strategies of DGA domains. Our system is evaluated and compared with the state-of-art system FANCI and two deep-learning methods CNN and LSTM, with data from a large university's network named TUNET. The results suggest that our system outperforms the strong competitors by a large margin on multiple metrics and meanwhile achieves a remarkable speed-up on model updating.