Steganography is to hide secret information in a normal cover, so that the secret information cannot be detected. With the rapid development of steganography, it's more and more difficult to detect. Steganalysis is the counter of steganography. In order to improve the detection effect, more complex high-dimensional features are proposed for steganalysis. However, this also creates huge redundancy features, which in turn consume generous time. Feature selection is a technique that can effectively remove redundant features. In this paper, we propose a new blind image steganalysis algorithm to distinguish stego images from cover images using a nature-inspired feature selection method based on the binary bat algorithm(BBA). Meanwhile, SPAM and several classifiers have been used to improve the detection effect. Furthermore, we select the ideal feature subset using BBA from the original features and use the selected feature subset to train the several classifiers. The experimental results demonstrate that our proposed method can improve the detection effect and reduces the redundant features.