In order to provide an important reference for vehicle scheduling optimization and truck reservation system design, a shortterm prediction method of truck turnover time based on data mining was established by using the collected data of a port in Shenzhen. Firstly, by analyzing the data of the terminal gate, the truck operation characteristics such as vehicle arrival time distribution, task type, the operation mode and the turnover time of the truck in the terminal were obtained. On this basis, the mapping relationship between truck operation characteristics and turnover time was established by using recurrent neural network (RNN) and training set data. Secondly, in order to reduce the impact of random fluctuations on the prediction of turnaround time, the wavelet decomposition algorithm was used to separate the residual of the fitting results of the recurrent neural network with highfrequency noise, and the filtered lowfrequency series was fitted by the autoregressive model. Finally, the combined model of the fitted cycle neural network and the autoregression model (AR) was established to support the shortterm prediction of the truck turnaround time, and verified by test set data. The results show that the combined model can greatly improve the prediction accuracy compared with the single RNN.