For the truck appointment system in a container terminal, optimizing the configuration of gate lane and yard crane based on the appointment information is the key to shorten the external truck waiting time and reduce the redundancy of terminal resource. A hybrid approach combining deep neural network and optimization model is proposed. The deep neural network is applied to predict the truck waiting time in the yard based on the yard data. The optimization configuration model for gate lane and yard crane is established by combining the predicted result. The average waiting time of trucks, the configuration of gate lanes and yard cranes before and after optimization are compared. The results show the effectiveness of the proposed approach, which also provides a new road map for optimizing container terminal resource configuration.