Abstract: A quantitative statistical characterization method of micron-level second phase in aluminum alloy based on deep learning is disclosed. The method includes obtaining a feature database of the standard sample, training the feature database by the image segmentation network U-Net based on deep learning to obtain a U-Net segmentation model, selecting the corresponding parameters of the optimal precision and establishing a U-Net target model; clipping the aluminum alloy image to be detected and inputting the clipped images into the U-net target model, obtaining the size, area and position information of the second phase through the connected region algorithm, carrying out statistical distribution of the data set combined with the mathematical statistical method, and restoring the position information to the surface of the aluminum alloy to be tested to obtain the full-field quantitative statistical distribution and visualization results.