The aircraft dynamics and control has achieved a significant progress, but also faces a series of problems that need to be dealt with. Deep learning provides a new solution for these problems, and it is good in many aspects, such as the working model of the experience storage, the intelligent accumulation and the off-line training. In this study, around the subject of the autonomy and the intelligence enhancement for the flight control, the applications of the deep learning in aircraft dynamics and control are reviewed in three aspects: (1) applications of deep learning in dynamic modeling to improve the computational efficiency and accuracy of modeling, or to solve the problem of inverse dynamics; (2) applications of deep learning in optimal control to improve the speed of trajectory planning or the real-time performance and autonomy of flight control; (3) applications of deep learning in mission design to improve optimization speed and decision-making intelligence. Furthermore, the advantages and the disadvantages are analyzed and representative papers are introduced. Finally, four suggestions to apply the deep learning in aircraft dynamics and control are given.