Overview: Atmospheric polarization mode has important application value in the field of the autonomous navigation because of its stable meridian characteristics. However, its acquisition is limited by the physical characteristics of the acquisition device and is easy to be blocked by the surrounding environment of the acquisition location and thin clouds, resulting in the reduction or disappearance of the local atmospheric polarized light between the acquisition device and the shelter (buildings, trees, thin clouds, etc.). When capturing the atmospheric polarization information, it often produces the degradation of the atmospheric polarization characteristics in irregular areas and destroys the overall structure of the atmospheric polarization mode. As a result, the accuracy of its meridian decreases. To solve this problem, this paper proposes a neighborhood constrained atmospheric polarization mode generation network, which combines the neighborhood constraint characteristics of the atmospheric polarization information. It carries out multi-step neighborhood feature reasoning through the neighborhood feature repair module, and gradually propagates the continuous distribution characteristics of the atmospheric polarization information to the missing region, so as to increase the feature constraints in the reconstruction process. In addition, this paper further puts forward the constraint condition on the physical characteristics of the atmospheric polarization mode - Solar meridian angle loss. The solar meridian feature that generates atmospheric polarization information is extracted by the solar meridian feature constraint, and compared with the solar meridian feature of the real atmospheric polarization mode, so as to guide the generation process and improve the meridian accuracy of reconstruction results. Finally, because the acquisition of atmospheric polarization modes is limited by time, space and the number of the acquisition equipment, it is difficult to directly obtain the local atmospheric polarization modes under different conditions at the same time. Therefore, this paper proposes a binary mask data set containing four distribution types, which combined with the measured global atmospheric polarization data. It can also simulates the local effective atmospheric polarization information under different conditions and improves the diversity of local atmospheric polarization mode data. In this paper, experiments are carried out on the measured atmospheric polarization data and compared with other latest methods. The experimental results show the robustness and superiority of this method.Atmospheric polarization mode supports the polarization navigation application by virtue of the "∞" feature containing the solar meridian information. However, due to the limitation of the physical characteristics of the acquisition device, the surrounding environment of the acquisition location and the occlusion of thin clouds, the obtained atmospheric polarization information is partially distorted and the accuracy of the solar meridian is reduced. In order to solve this problem, this paper proposes an atmospheric polarization pattern generation network based on neighborhood constraints. The network mines the continuity of atmospheric polarization pattern distribution, increases the constraints of reconstruction process through multi-step neighborhood feature reasoning, and accurately generates global atmospheric polarization information from local effective polarization information. In addition, according to the physical characteristics of the atmospheric polarization mode, the angle loss of solar meridian is proposed to further improve the accuracy of the solar meridian. In this paper, experiments are carried out on the measured atmospheric polarization data, and compared with other latest methods. The experimental results show the robustness and superiority of this method.