The temperature measurement error caused by solar radiation, that is, the solar radiation error, can be as high as 1 K. To improve the temperature measurement accuracy, and to address the problem of high power consumption of traditional forced ventilation temperature measurement device, a temperature sensor device for brushless DC (BLDC) fan ventilation is designed, which combines natural ventilation and forced ventilation functions, effectively reducing power consumption. The computational fluid dynamics (CFD) method is utilized to conduct multi-physical field fluid-structure coupling simulation of the sensor and quantify the radiation error under different conditions, and the functional mapping relationship between wind speed and solar radiation intensity and the suction pressure of the wind turbine is established by minimizing the radiation errors, and formulate the wind turbine control strategy. The genetic algorithm-optimized BP neural network (GA-BP) algorithm optimized by genetic algorithm was compared and selected to train and fit the simulation data set, thereby constructing the radiation error correction equation. Finally, through the field comparison experiment with the 076B temperature sensor, it is shown that the radiation error of the designed temperature sensor after algorithm correction can be controlled within 0.05 K, the mean absolute error was 0.039 K, and the root mean square error was 0.045 K.