Pedestrian trajectory prediction is a crucial task for many domains, such as self-driving, navigation robots and video surveillance. The performance of trajectory prediction can be improved in various patterns, including using a more effective network, considering more complicated social interactions, and utilizing sufficient information. On the one hand, the change of subsequent trajectory depends on the geographical scene and the social interaction with other pedestrians in the same scene. On the other hand, the subsequent trajectory also makes some real-time adjustments according to the judgment of pedestrian behavior. Therefore, we propose a novel behavior recognition module to obtain extra pedestrian behavior information. To guarantee the precision and diversity of prediction, this paper builds the Geographical, the Social and the Behavior feature modules based on the GAN framework to process information. As a result, we present a trajectory prediction approach, referred to as the BR-GAN, which exploits geographical, social and behavior context-aware. The BR-GAN achieves greater accuracy in parts of the ETH/UCY datasets compared with some baselines. We will republic all of them on https://github.com/HITjian/Pedestrian-trajectoty-prediction-based-on-behavior-recognition.