The inversion of physical and chemical properties of satellite soil is the most important part of deep space exploration, and thermal properties such as thermal conductivity and heat capacity parameters are the scientific basis for studying the composition of satellite soil, and temperature measurement is an important parameter for in-situ detection of satellite soil based on penetration. In this paper, the surface temperature inversion method of lunar soil probe based on LSTM neural network algorithm is studied to solve the problem that the surface temperature of lunar soil probe can not be measured directly. The penetration process was simulated by ANSYS/LS-DYNA finite element software to obtain the temperature data of multiple groups of reconnaissance warhead. The data were selected according to the finite difference method of discrete heat conduction equation, and the inversion model was established by using the long and short term memory neural network. The root-mean-square error of the inversion curve is 12.9 ℃ and the maximum relative error is less than 10% compared with the experimental curve. The experimental results show that the method proposed in this paper can realize the inversion of the outer surface temperature of the probe.