People flow data are important for economic prediction and urban planning development. In this study, we deployed a Swin Transformer on panoramic street view images and constructed a wide-area people flow trend model using image data. First, for people flow trend prediction, we concentrated the flow data of a wide area and counted it into mesh units, and defined people flow level by the number of people on each mesh unit. Second, to construct the dataset, we determined which mesh unit each image belonged to by the locations of photography and coordinated range of mesh units to define people flow trend level for each image. Then, after category balance and random selection from wide-area data, we implemented Swin Transformer and trained models in this small part of the data, and made the precision evaluation and output a 75.84% accuracy model. Finally, we deployed our model in the total study area data and contributed to the people flow trend model.