The port amount of import and export goods can reflect the congestion of port flow, whose accurate prediction would provide suggestions for port management to make reasonable decisions. In this study, the Seq2Seq model in the field of machine translation is used to model various factors that affect the amount of goods inflow and outflow from the port. An Seq2Seq model can reflect the change of the amount of import and export goods in the time dimension and describe the influence of external factors such as weather and holidays, so as to make accurate predictions. An Seq2Seq model consists of two LSTM, respectively acting as an encoder and a decoder. It can capture the changing trend of containers in the short and long term and predict the amount of goods in the future based on historical import and export volume. Experiments were carried out on a real-world dataset of import and export containers in Tianjin Port. The experimental result reveals that the deep learning prediction model based on Seq2Seq is more effective and efficient than traditional time series model as well as other existing machine learning prediction models.