The ground temperature changes at a rate of 0.1 ℃ every 10 years,however,a solar radiation error of about 1 K can be produced by conventional radiation shields due to the influence of solar radiation.In order to improve the accuracy of surface temperature measurement and reduce working energy consumption,this paper designs a temperature sensor based on the accelerated diffusion of radiant heat via piezoelectric ceramic vibration.First,the Computational Fluid Dynamics (CFD) method is used to calculate the radiation error of the temperature sensor under multi physical factors,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 real environment to verify the feasibility of the scheme.The experimental results show that the absolute error and root mean square error between the corrected value and reference value of the surface temperature sensor are 0.041 ℃ and 0.055 ℃,respectively,which also verifies the superior correction performance of the neural network algorithm.