Inverse Synthetic Aperture Radar(ISAR) images are usually affected by various noises which degrade the quality of ISAR images and seriously affect the subsequent feature extraction and target recognition applications. Improving the image quality and reducing noise interference have become important steps in the application of target recognition. A method based on Constant False-Alarm Rate (CFAR) detection and density clustering is proposed to suppress speckle interference and transverse fringe interference in ISAR images. Compared with traditional methods, this method can effectively preserve the details of the target while ensuring the effect of interference suppression. The image area, length and Doppler spread are extracted as ISAR recognition feature vectors. Field experiments show that the proposed method effectively suppresses the interference components in the image, retains more image details, and effectively improves the stability of ISAR recognition features.