Knowledge Graphs (KGs) provide human knowledge with nodes and edges being entities and relations among them, respectively. Multi-hop question answering over KGs---which aims to find answer entities of given questions through reasoning paths in KGs---has attracted great attention from both academia and industry recently. However, this task remains challenging, as it requires to accurately identify answers in a large candidate entity set, of which the size grows exponentially with the number of reasoning hops. To tackle this problem, we propose a novel Deep Cognitive Reasoning Network (DCRN), which is inspired by the dual process theory in cognitive science. Specifically, DCRN consists of two phases---the unconscious phase and the conscious phase. The unconscious phase first retrieves informative evidence from candidate entities by leveraging their semantic information. Then, the conscious phase accurately identifies answers by performing sequential reasoning according to the graph structure on the retrieved evidence. Experiments demonstrate that DCRN significantly outperforms state-of-the-art methods on benchmark datasets.