Focus on the problems that the airflow in the traditional radiation shield is not easy to circulate and the energy consumption of its work is relatively large, a design of a meteorological temperature sensor with low radiation error is proposed, which combined natural ventilation and forced ventilation. Firstly, Computational Fluid Dynamics (CFD) simulation is used to analyze the radiation error optimization design under different environmental variables and fan speeds. Secondly, the support vector regression (SVR) algorithm is used to train the simulation results to obtain the prediction model. Finally, an outdoor experimental test platform is built to verify the feasibility of the design and the measurement accuracy of the prediction model. The experimental results show that the proposed meteorological temperature sensor can reduce the measurement radiation error to less than 0.05 ℃, which has a significant effect on reducing radiation and the accuracy of the prediction model is high, and the RMS error between the experimental value and the measurement value of the 076B forced ventilation radiation shield is 0.185 ℃, and the RMS error with the predicted value is 0.129 ℃.