The visual environment has long been regarded as an important factor affecting traffic safety． However， limited by image analysis methods，existing studies on the relationship between environmental visual factors and traffic safety are mainly qualitative and it is difficult to conduct large-scale quantitative analysis of the visual environment． This study used rich，easy-to-extract and growing streetscape images as the data source of environmental factors，and extracts the image feature information，location information and sensory information of streetscape through methods such as expanded residual network ( DＲN) ． And Pearson correlation coefficients and ridge regression were used to screen quantitative indicators to construct a quantitative analysis framework for the association between visual environment and traffic safety based on deep learning，providing a new quantitative means to study urban landscapes． In addition，statistical analysis methods were used to identify the influencing factors that lead to changes in road traffic safety conditions． On the one hand，the variability in the contribution of influencing factors associated with the urban landscape to accident rates in different urban areas was explored． For example，an increase in the proportion of ″vegetation″ in commercial and older urban areas has a positive impact on traffic safety， but the opposite is true in suburban areas． On the other hand，patterns that may advance urban planning theory are also discovered． For example，the closer the road unit is to the city center，the safer the traffic conditions． This paper offered a new way of thinking about the link between urban landscape and traffic safety in quantitative terms， and offered the possibility for assessing urban traffic safety conditions efficiently and on a large scale．