Visual acuity is one of the most important indicators of group health and a vital survey content of building a healthy city. Traditional methods of investigating group vision have limitations. In this study, the pedestrian's face attributes in the surveillance video are analyzed by deep learning. We identify the number and proportion of visual impairments in public group and study them by gender, and use it as a sample index of regional population health. Aiming at the problem of face attributes recognition in video, the convolutional neural network for face detection is introduced to detect pedestrian's face. On this basis, an improved convolutional neural network for face analysis is proposed to recognize gender and whether or not to wear glasses. Finally, a regional visual data display system based on Baidu Map is established, and the visual data of the proportion of male and female visual impairment is displayed in streets and regions of the Web, which lays the foundation for the next practical application. The experimental results and system demonstration show that the proposed method can effectively identify group visual impairment and provide a new idea for group visual health survey.