Abstract: Shale pores are the main space of shale gas reservoir. The shape, size, connectivity and development degree of pores may determine the performance of reservoir to a great extent. A full understanding of shale pores is critical before exploitation. At present, obtaining structure parameters of shale pores based on threshold segmentation method plays an important role in shale microstructure characterization. However, due to the difference in gray distribution of SEM images, this method requires modification of the best segmentation threshold individually to achieve the best pore segmentation effect, and the threshold segmentation method cannot classify the pore directly. It may cause trouble to the subsequent quantitative characterization of shale microstructure. In order to realize the intelligent recognition and classification of shale pores, a Deep Convolution Neural Network FLU-net based on pixel-level semantic segmentation is designed in this paper. The pores of shale are identified and classified into organic pores, inorganic pores (intragranular pores and intergranular pores) and fractures. Combined with the statistical method of pore scale classification, the number of pores, pore size, porosity and other parameters for different types of pores are analyzed, and thus automatic quantitative characterization of micro pore structure of shale reservoir is realized. This paper takes the Scanning Electron Microscope (SEM) images of shale from the Zu-201 well area, Yuxi block in Chongqing and the Wei-204 well area, Weiyuan area in Sichuan Basin as the research objects. After manually labeling and dividing the original dataset of 1600 SEM images of shale, FLU-net is used for pore recognition. The results show that this method not only keeps high accuracy, but also has higher automation and generalization ability than traditional prediction methods. Therefore, the combination of SEM and semantic segmentation model based on Deep Learning is an effective mean for quantitative study of shale microstructure characterization.