Abstract: In order to solve the problem that state refinement for LSTM (SR-LSTM) does not consider the influence of surrounding physical scenes on pedestrian trajectory prediction, and can not generate a variety of possible samples, a social and scene awareness pedestrian trajectory prediction model based on GAN is proposed. This model introduced social attention and semantic pool mechanism, and social attention mechanism was used to model the current important intention of adjacent pedestrians in order to select important information from adjacent pedestrians. Semantic pools defined the semantics of physical scenes and learn their correlation with pedestrian tracks. Because GAN was prone to mode collapse and decline, Info-GAN was used for antagonistic training to generate more real samples. The experiments on ETH and UYC data sets show that compared with SR-LSTM, ADE of this method is 8.9 percentage points lower and FDE is 12.8 percentage points lower, and more reasonable samples can be generated.