The ground penetrating radar technology has been widely used in the rapid and precise detection of hidden diseases in urban roads and underground spaces. However, due to the complex interference of urban environment, the ground penetrating radar data is mixed with noise and clutter, resulting in low signal-to-noise ratio of data and affecting the processing and identification accuracy. In order to improve the signal-to-noise ratio of ground penetrating radar data and obtain high-quality detection data, this paper proposes an improved wavelet threshold denoising algorithm based on particle swarm optimization algorithm on the basis of the traditional wavelet threshold denoising algorithm. Through the use of MATLAB and gprMax2D tools to carry out the denoising simulation experiment. The experimental results show that compared with the traditional soft and hard threshold denoising algorithms, the signal-to-noise ratio is increased by 28.02% and 6.97% respectively, and the mean square error is reduced by 71.86% and 31.88% respectively, which has better denoising effect. Applying the algorithm proposed in this paper to the data processing process of ground penetrating radar can provide technical support for the safety of urban roads and underground space.