In detecting cluster targets in ports or near-shore waters, the echo amplitude is seriously disturbed by interface reverberation, which leads to the distortion of the traditional target intensity characteristics, and the appearance of multiple targets in the same or adjacent beam leads to fuzzy feature recognition. Studying and extracting spatial distribution scale and motion features that reflect the information on cluster targets physics can improve the representation accuracy of cluster target characteristics. Based on the highlight model of target acoustic scattering, the target azimuth tendency is accurately estimated by the splitting beam method to fit the spatial geometric scale formed by multiple highlights. The instantaneous frequencies of highlights are extracted from the time-frequency domain, the Doppler shift of the highlights is calculated, and the motion state of the highlights is estimated. Based on the above processing method, target highlights’ orientation, spatial scale and motion characteristics are fused, and the multiple moving highlights of typical formation distribution in the same beam are accurately identified. The features are applied to processing acoustic scattering data of multiple moving unmanned underwater vehicles (UUVs) on a lake. The results show that multiple small moving underwater targets can be effectively recognized according to the highlight scattering characteristics.