The accurate flight delay prediction results can provide a great reference value for the prevention of large-scale flight delays. The flight delays prediction is a time-series prediction in a specific space， however most of the existing prediction methods are the combination of two or more algorithms， and there is a problem of fusion between algorithms. In order to solve the problem above， a Convolutional Long Short-Term Memory （Conv-LSTM） network flight delay prediction model was proposed that considers the temporal and spatial sequences comprehensively. In this model， on the basis that the temporal features were extracted by Long Short-Term Memory （LSTM） network， the input of the network and the weight matrix were convolved to extract spatial features， thereby making full use of the temporal and spatial information contained in the dataset. Experimental results show that the accuracy of the Conv-LSTM model is improved by 0.65 percentage points compared with LSTM， and it is 2.36 percentage points higher than that of the Convolutional Neural Network （CNN） model that only considers spatial information. It can be seen that with considering the temporal and spatial characteristics at the same time， more accurate prediction results can be obtained in the flight delay problem. In addition， based on the proposed model， a flight delay analysis system based on Browser/Server （B/S） architecture was designed and implemented， which can be applied to the air traffic administration flow control center.