Nowadays, the steganalysis of digital image mainly focuses on the design of steganalysis features to improve the universal blind detection (UBD) model's detection accuracy. However it has nothing to do with the testing images and is difficult to achieve high-precision detection. Based on large data training resources, this article studies the influence of steganography on image features to uncover the important relationship between steganalysis and image feature. Furthermore, the article proposes a steganalysis method for testing samples to select specialized training sets. The classical JPEG steganography algorithm nsF5 and the mainstream JPEG steganalysis features, such as CC-PEV, CC-Chen, CF^*, DCTR and GFR, are used as an example to organize the experiments. The results show that the accuracy of this method is higher than that of other similar methods.