In response to the increasing demand for scientific exploration in proximity space, a temperature sensor for nearspace sounding instruments has been devised, predicated upon miniature bead-shaped thermistors. This methodology encompasses several pivotal stages: Primarily, the Computational Fluid Dynamics technique was enlisted to simulate and quantify the solar radiation error entailing the sensor probe. Subsequently, a backpropagation network and optimized through a genetic algorithm-based backpropagation neural network were employed to train on the accumulated dataset, thus comparing and facilitating the construction of a predictive model. Furthermore, a low pressure wind tunnel experimental platform was erected to emulate conditions reminiscent of those in the near-space milieu. This permitted the evaluation of solar radiation errors under diverse parameter configurations. The obtained test results were then compared with the data output from the predictive model to validate the precision of the sensor′s measurements. The experiments revealed that the average measurement error of the sensor probe was 0.007 3 K, with a root mean square error of 0.009 8 K.