In existing resource allocation schemes for aeronautical Ad hoc networks,there remain some problems that high control overhead is high and time slot is low in utilization in trans-oceanic scenario applications,an improved learning automata is proposed for slot allocation (ILASA) scheme based on the clustered aeronautical Ad hoc network.Firstly,a model of clustered aeronautical Ad hoc network is given. Secondly,a time slot frame structure is designed,the time slot allocation mode of the learning automata algorithm is improved,and the probability updating method in the reward and punishment mechanism is optimized,solving the probability selection bias problem of the learning automata algorithm by increasing the time slot reservation mechanism.Lastly,the network model is constructed on the basis of the OMNeT++ platform for simulation.The results show that the proposed scheme can reduce the resource overhead caused by the control information,effectively reduce the average end-to-end delay of the network,and im prove the network throughput and packet delivery rate.