[Purposes] Identify the black spots of road traffic accidents and explore the causes of the accidents in black spots area. [Methods] Combining geographic information software and interpretable machine learning algorithm, this paper proposes a method of black spot identification and analysis considering temporal and spatial attributes of traffic accidents. [Findings] The accident density in the area with high accident incidence was mainly concentrated in the vicinity of large commercial area, passenger station and industrial area. The accident density in the area with high accident incidence was 6.61 times that in the area with low accident incidence. In areas with high accident rates, collision patterns, weather, visibility, lane type and two-wheeler type play a decisive role. In the low accident area, collision form, road class, visibility, road material and two-wheel vehicle type are the main influencing factors. In addition, factors such as intersection section type, lane type, two-wheeled vehicle type and hit-and-run have different impacts in different accident areas. [Conclusions] There are black spots in urban traffic accidents, and the influence of some road environmental factors is inconsistent in different accident areas. The results can provide guidance for the traffic management department to formulate preventive measures for the black spot area.