Different targets have different material parameters on their surfaces. In the physical property inversion of targets, the contact measurement method is difficult to be carried out in complex environments, while the non-contact measurement method, due to certain errors in the measurement data compared with the contact measurement, causes the inversion accuracy to be affected. Therefore, it is necessary to propose a surface physical property inversion method for non-contact targets.In this paper, a non-contact target surface physical property inversion method of infrared laser echo is proposed (Fig.1). The laser echo intensity measurement system is built (Fig.7). First, six materials (Fig.4) and seven measurement distances were selected. Through the 4.6 μm infrared laser transmitter, the laser is launched to the material at a certain distance away, and after the reflection of the material surface, the laser echo intensity information is collected by the receiver to establish a database of the laser echo intensity on the material surface; second, the SSA-GRNN neural network is used to obtain the prediction model of the laser echo intensity on the material surface; lastly, the echo intensity information of the unknown material is measured, and by assigning the material Finally, the echo intensity information of the unknown material is measured and input into the prediction model by assigning the material type, calculating the error between the predicted echo intensity value and the real value, and obtaining the material number with the smallest error to invert the material properties of the unknown target surface.The measured echo intensity data were used to train the SSA-GRNN echo intensity prediction model, and the model established by the SSA-GRNN generalized regression neural network not only has strong generalizability, but also has high accuracy. The echo intensity data on the surface of the unknown target at five distances are measured (Tab.3), and the predicted values of echo intensity as well as the results of physical property inversion are obtained by assigning the material type number to material 1 as an example (Tab.4). The experimental results (Fig.10) demonstrate that the root mean square error of the echo strength prediction results is reduced from 11.337 for the conventional network to 2.482 for the optimized one with the same inversion target. The relative inversion accuracy of the optimized neural network model can reach more than 88.89%, and the average inversion accuracy is improved by 45.83% compared with the traditional method, which is a better inversion effect.Aiming at the current material physical property inversion using contact measurement and the existence of low inversion accuracy and other problems, a non-contact target surface physical property inversion method based on infrared laser echo is proposed. The inversion method proposed in this paper effectively solves the problem of local optimal solution in the traditional GRNN network inversion. At the same time, this paper adopts the non-contact target surface echo intensity measurement method, through the infrared laser irradiation of the target surface, the laser echo intensity signal is collected, and the target surface echo intensity data are calculated. Compared with the traditional contact echo intensity measurement, the distance is nearer and the environmental requirements are higher, the non-contact echo intensity measurement in room temperature environment is realized. The overall inversion method of the article has certain robustness and universality, which is of great significance for inverting the surface physical properties of non-cooperative targets. Since the 4.6 μm infrared laser transmitter is used in this paper, the next step is to choose infrared light sources in other wavelength bands to analyze the effect on the laser echo intensity on the target surface and verify the applicability of the laser echo intensity physical properties inversion method in different wavelength bands.