  With the rapid development of civil aviation and the significant improvement
of people's living standards, taking an air plane has become a common and
efficient way of travel. However, due to the flight characteris-tics of the
aircraft and the sophistication of the fuselage structure, flight de-lays and
flight accidents occur from time to time. In addition, the life risk factor
brought by aircraft after an accident is also the highest among all means of
transportation. In this work, a model based on back-propagation neural network
was used to predict flight accidents. By collecting historical flight data,
including a variety of factors such as meteorological conditions, aircraft
technical condition, and pilot experience, we trained a backpropaga-tion neural
network model to identify potential accident risks. In the model design, a
multi-layer perceptron structure is used to optimize the network performance by
adjusting the number of hidden layer nodes and the learning rate. Experimental
analysis shows that the model can effectively predict flight accidents with
high accuracy and reliability.
