Abstract: In order to achieve feedback optimization of parameters under tunnel construction in rich water zones, a feedback analysis method for tunnel parameters in fluid-solid coupling conditions was established based on the machine learning and intelligent optimization algorithm. Firstly, the numerical calculation model was modelled and solved by the fluid-solid coupling model. The displacement of adjoining rock and the distribution of pore water pressure in different combination of rock parameters were obtained by orthogonal design analysis, and the learning samples for machine learning were established, in which the input group was consisted of a set of rock parameters and the output group was consisted of adjoining rock displacement and pore water pressure. Then, Gaussian Regression Process was used to obtain the non-linear mapping relation contained the learning samples, and differential evolution algorithm was used to optimize the key parameters involved in the process. Furthermore, according to the established regression model and the measured displacement and pore water pressure in the target zone, the differential evolution algorithm was used to optimize the rock parameters and the parameter feedback analysis results were obtained. Finally, the inverse values were compared with the actual measured values, and the reliability of the adjoining rock parameters obtained from the feedback analysis was verified.