The ground temperature changes at a rate of 0.1 K every 10 years, however, due to the influence of solar radiation, when measured in practice, conventional radiation shields sensor will produce a solar radiation error of about 1 K. In order to improve the accuracy of surface temperature measurement and reduced working energy consumption, this paper designs a temperature sensor based on the diffusion of radiant heat from a piezoelectric ceramic vibration acceleration sensor probe. Firstly, the Computational Fluid Dynamics (CFD) method is used to calculate the radiation error of the temperature sensor under multi physics factors, and then the data is fitted and analyzed using the neural network algorithm, and finally the field experiment platform is built to place the temperature sensor in the real environment to verify the feasibility of the scheme. The experimental results show that the absolute error and root mean square error of the correction value and reference value of the surface temperature sensor are 0.041 K and 0.055 K, respectively, which also verifies the superiority of the correction effect of the neural network algorithm.