Aiming at the problem that it is difficult to improve the accuracy of departure flight delay prediction， a departure flight delay prediction model based on Deep Fully Connected Neural Network （DFCNN） was proposed. Firstly， on the basis of considering flight information， airport weather and flight delay history， the influence of flight network structure on prediction model was considered. Secondly， experiments were carried out from three dimensions of activation function， input data item and delay time threshold to optimize and verify the model ability to suppress gradient dispersion and improve the learning performance. Finally， through adjusting the vertical expansion method of the number of neural network layers and the Dropout parameters of the random loss layers， the generalization ability of the model was improved. The results of experiments indicate that the prediction accuracy of the proposed model can be improved by 1.26 percentage points and 1.28 percentage points respectively after using tanh and Exponential Linear Unit （ELU） functions in the proposed model than using Rectified Linear Unit （ReLU）. After considering the flight network structure， the prediction accuracy calculated by the proposed model using ELU function is improved by 3.12 percentage points than without considering the flight network structure. When the Dropout parameters are adjusted， the loss value of the model is continuously reduced with 60 min time threshold. With a 5-layer hidden layer network and a Dropout parameter of 0.3， the prediction accuracy of 92.39% can be achieved by the proposed model. Therefore， the proposed model can make more accurate judgments on domestic flight delays.