BGP route hijacking is a critical threat to the Internet. Existing works on path hijacking detection firstly monitor the routes of the whole network and then directly trigger a suspicious alarm if the link has not been seen before. However, these naive approaches will cause false positive identification and introduce unnecessary verification overhead. In this work, we propose Metis, a matching-and-prediction system to filter out normal unseen links. We first use a matching method with three rules to find out suspicious links if there is an unseen AS. Otherwise, we propose using a neural network to make a prediction based on the AS information at each end of the link and further quantify the suspicion level. Our large-scale simulation results show that Metis can achieve precision and recall of over 80% for detecting fake AS-PATHs. Moreover, our deployment experiences show that compared to state-of-the-art system, Metis can save 80% overhead.