This paper presents an application of natural computation integrating artificial neural network and Genetic algorithm in geo-stress field analysis of hydropower engineering. The artificial neural network (ANN) model is used to implement simulation analysis of the hydraulic fracturing behavior of rock mass instead of numerical computation, which can well reduce work load of numerical modeling calculation and figure out the integration problem between numerical computation and optimization algorithm. The only limited field test dataset can be applied to drill the data-driven artificial neural network model, which is one of the main advantages of ANN. The genetic algorithm (GA) is applied to perform multi-objective optimization for identification of geo-mechanical parameters by means of its objective function. The developed modeling framework is verified with field measurements in a practical project of hydropower engineering. Validation results illustrate that the proposed approach is capable and valuable in settling geo-mechanical parameters identification of nonlinear behavior problem.