The holding of large-scale events will lead to the concentration of a large number of people and vehicles in the surrounding affected areas in a short period of time, and the road network around the venue is differentiated from conventional traffic. To investigate the mechanism of the impact of large events and their characteristics on the operation of the road network around the venues. The study resolved the influence characteristics of factors such as event scale, spatial distance between road sections and event venues, and constructed an interpretable machine learning model integrating XGBoost algorithm and Partial Dependence Plots to capture the nonlinear effects and synergistic influences of different factors. The study conducted an empirical study with Beijing as an example. The heterogeneity of the single factor shows that the spatial distance from the road section to the event venue and the event scale have a greater impact on the operational state of the road network around the venue, with a relative importance of 27.1% and 25.4%, respectively; The time from the start/end of the event has obvious non-linear characteristics on the road network operation status around the venue, and the road sections within 3 km around the venue will be significantly affected within 30-60 minutes before the event starts and 30 minutes after the event ends. The synergistic effect of two-dimensional factors shows that when the scale of the event is larger than 30,000 people, holidays and adverse weather have a negative impact on the running state of the road network around the venue. Meanwhile, it is found that in rain and haze weather, the operation status of the road network around the venue is affected by time and space, and the influence range is the road section within 2.5km from the venue within 60 minutes before the start and 40 minutes after the end of the event. The findings of the study can provide quantitative data support for identifying the causes of road congestion and formulating scientific and effective road network control strategies during large events.