Aiming at the disproportionate characteristics of wildfire disasters near the transmission lines in time and space distribution, a method to assess the spatial-temporal distribution of wildfire risk in transmission line corridors is proposed based on dynamic Bayesian Network. Firstly, the data of 17 wildfire-related factors in three categories including anthropogenic, meteorologic, vegetation and geographical factors are collected. The important factors are screened by the random forest algorithm to reduce the input data dimension and model complexity. Then, a Bayesian network model is applied to eliminate the complex coupling relationship between factors, and on this basis, the monthly time scale dynamic Bayesian network wildfire risk assessment model is established. Compared with the Bayesian network model, the factors of the last time is incorporated to the dynamic Bayesian network to improve assessment accuracy. Next, the time scale is further refined, and it is found that with the decrease of time scale, the evaluation effect of the model is gradually improved. The accuracy of the dynamic Bayesian model reaches 86.39% on the 3-day timescale. Finally, the dynamic Bayesian network model is used to draw the distribution map of wildfire risk in Guangdong Province during the Qingming Festival in 2022, which provides the basis for the prevention of wildfires in the power grid.