Underwater optical imaging is an important way to implement the seabed exploration and target recognition. There occur a lot of bubbles due to the sea wave, ship wake, marine creatures’ swimming and breathing. The underwater target imaging effect is often limited by light scattering effect of bubbles, so it is difficult to identify targets, and the general optical technology is difficult to eliminate the bubbles’ influence on imaging. In this article from the bubble theoretical derivation and the bubble simulation, we investigate the changing trend of target’s polarization information under the condition of different light incident angles in the underwater environment, data gathering, data processing and data analysis, by using the polarimetric image fusion method to suppress the influence of bubbles to build a complete target imaging research system under bubble group environment in line with the above several big aspects. According to the above problem, in this paper, the change of light intensity and polarization information of incoming light in underwater single bubble, bubble group and target’s surface are investigated; the target imaging in the bubble group environment with the change of light incident angle and polarization imaging band on the basis of the construction of experimental platform of underwater bubbles is explored; the change trends of strength and polarization information with different metal targets are studied; the change trends of strength and polarization information of underwater target under thickness of different bubble groups are analyzed; finally the underwater target images under the condition of different imaging resolutions and the using of fusion methods of polarization feature extraction and visual information of image to suppress the bubble influence on underwater target imaging are studied. The experimental results show that the target imaging under bubble group environment is influenced by many factors, and using polarimetric image fusion method can well weaken the bubble group’s influence on imaging, and improve the clarity of underwater target. In view of difficult problems about target identification existing in the high-density bubble group environment, we will use energy loss compensation or machine learning method to realize the target recognition and image restoration in the future.