Despite all the benefits 60 GHz networks bring about, such as high network bandwidth, effective data rates, etc., one of its main application scenarios, Line-of- Sight (LOS) communications, still has troubles in actual indoor environments due to its high directionality. Traditional beam training methods are inaccurate and time-wasting, leading to unstable and inefficient wireless networks. Therefore, in this paper, we attempt to address this problem from a new aspect, i.e., assisting the signal adaptation with human mobility prediction. A state-of-the-art long short-term memory (LSTM) model is adopted to analyze the past trajectories and predict the future position, which can serve as an important reference for the transmitters to proactively adjust their beams and provide seamless coverage. In addition, we also design an algorithm to optimize the beam selection problem and improve the network quality. To the best of our knowledge, this is the first work in the field to use deep learning models for the beam selection problem. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.