The invention proposes a dangerous driving behavior real-time detection method based on deep learning, and the method comprises the steps: building a front car image collection system, collecting a training sample, carrying out the manual marking, and building a dangerous driving behavior data set; proposing a spatial pyramid pooling convolution depth confidence normalized classification network (SPP-CDBRNet) model according to the characteristics of the dangerous driving behavior data set based on a deep learning method; carrying out the preprocessing of the built data set, carrying out the training of the SPP-CDBRNet model through a random gradient descent method with a momentum and the data set after preprocessing, and obtaining an SPP-CDBRNet which precisely recognize whether there is a dangerous driving behavior (behaviors of using a cellphone and smoking during driving); carrying out the detection of a front car image through the determined SPP-CDBRNet model, and achieving the real-time detection of the dangerous driving behavior. The method can effectively improve the detection precision of dangerous driving behaviors, is good in instantaneity and mobility, and is good in application prospect.