In order to quantitatively depict the dynamic fluctuations and gravity of driver behavior,a method for constructing and analyzing a bad driving behavior spectrum was studied for real-time identification of bad driving behavior. Firstly, based on the Lagrange interpolation method, trajectory data was cleaned and feature indicators were extracted to construct a driving behavior spectrum. The risk measurement method was used to quantify four types of bad driving behaviors, including abrupt turning, rapid acceleration, sudden deceleration, and overspeeding. Secondly,The IQR of a large sample statistical distribution and the CRITIC method with objective weighting were used to determine the threshold values and weights of the feature indicators for bad driving behavior. A fuzzy comprehensive evaluation model was constructed using a membership degree function to determine the characteristic values of bad driving behavior spectrum and calibrate bad driving vehicles. Then, based on the bad driving behavior spectrum characteristic values as input, an artificial intelligence convolutional neural network (CNN) algorithm was used to identify bad driving behavior, and the identification error was compared with traditional machine learning algorithms[ ] such as SVM, RF, and BP. The results showed that the theoretical error value MAE of the CNN algorithm for identifying bad driving behavior was 0.059, RMSE was 0.084, and R2 was as high as 0.911. Therefore, combining the bad driving behavior spectrum as an objective quantitative method for bad driving behavior with the CNN algorithm can automatically identify bad driving behavior based on vehicle operating trajectory, and has objectivity, reliability, and adaptability.