In order to improve the safety level of non-motor vehicle traffic at signalized intersections, based on the survey data of signalized intersections with overlapping phase control in Guangzhou, this study analyzed the influencing factors of non-motor vehicle crossing behavior based on the C5.0 decision tree algorithm. Considering the influence of different periods on the crossing behavior of non-motor vehicles in the signal cycle, the study divided a complete signal cycle into four risk periods according to the risk conflict of non-motorized vehicles crossing the street, namely, the opposite green light risk period, the same direction green light safety period, the same direction green light risk period and the vertical direction risk period. And it divided the crossing behavior into three categories according to the waiting selection of non-motor vehicles at the intersection and whether or not to run red-light, namely, risky, opportunistic and law-abiding. It studied the influencing factors of the three types of crossing behavior by constructing a C5.0 decision tree model and analyzed and evaluated the classification effect of the model. The results show that the overall accuracy of the model classification results is greater than 83.04%, the AUC is greater than 0.880, and the model prediction accuracy is good. The crossing behavior of non-motorized vehicles at signalized intersections with overlapping phase control is mainly significantly related to the traffic environment, while the factors related to the rider’s behavior are less significant. The arrival risk period, non-motor vehicle signal light facilities, conflicting motor traffic flow, number of lanes and crossing risk have significant impacts on the occurrence of risk-taking crossing behavior, among which the arrival risk period is the most important influencing factor. The number of lanes, red-light time and arrival risk period have significant impacts on the occurrence of opportunistic crossing behavior, among which the number of lanes is the most important influencing factor. The conflicting motor traffic flow flow, signal period, number of lanes, crossing area and crossing risk have significant impacts on the occurrence of law-obeying crossing behavior, among which the conflicting motor traffic flow is the most important influencing factor.