Power distribution network accident in actual application scenarios account for more than 80% of total grid accident, and the prediction of power distribution network accident has always been a difficult issue. This study, under the call of “Ubiquitous IoT” proposed by the State Grid, analyzes the research results of scholars on this issue, and proposes an accident prediction method for power distribution network based on graph neural network with the idea of graph neural network. Referring to the commonly used graph neural network design framework, the node information aggregation function, prediction function, and loss function are designed in detail, and reasonable depth parameters are selected according to the algorithm flow test. The algorithm fully considers the mutual influence between connected nodes, and uses the real grid operation data to compare the two other algorithms commonly used in this field. Experiments show that the proposed algorithm improves the accuracy by 3.0% and is more robust.