Shortwave source localization represents a significant and pervasive aspect of research and development in many fields. Traditionally, the staff usually identify the direction of arrival of signal according to their own experience in shortwave signal direction-finding and localization. Recently, artificial intelligence technology such as deep learning has been applied to this work. However, deep learning requires a large number of signal samples and manual labeling. In our research, we propose a shortwave intelligent direction-finding and localization method based on reinforcement learning. It can reduce manual annotation, achieve the autonomous evolution of learning while working. Therefore, our work is about innovating DQN to this work. Through training, the feasibility and effectiveness of our approach was demonstrated.