## 6 Conclusions  
This study utilizes FLAC3D to establish 217 models, investigates the influence of anchor rod parameters and shotcrete layer parameters on the energy evolution characteristics of surrounding rock, and fits the results through machine learning models. The main conclusions are as follows:  
(1) The energy evolution characteristics of the surrounding rock are more sensitive to the isotropic and thickness parameters of the shotcrete layer support structure, and the cross-sectional-area, Young's modulus, and grout-stiffness parameters of the rock anchor support structure, respectively. (2) The parameters of shotcrete layer support structure are not the bigger the better. When isotropic is 100 GPa, the peak values of elastic strain energy and plastic dissipation energy are higher than the value when isotropic is 25 GPa. In addition, for rock anchors, when the value of cross-sectional-area (more than 0.005024 m\({}^{2}\)) is too large, the value of plastic dissipative energy in the surrounding rock starts to show an increasing trend. (3) Appropriate support design parameter schemes can reduce the peak value of energy (elastic strain energy, dissipated energy) and at the same time change the distribution characteristics of energy. Therefore, the design of the support program from the perspective of energy may be able to improve the stability control effect from the root. At the same time, analysis based on the Kendall correlation coefficient found that there was a significant positive correlation between Grout-stiffness and Pre-tensioning Force. (4) By introducing BP, DT, ELM, RF, and SVM, the prediction models of elastic strain energy and plastic dissipative energy of surrounding rock are constructed. In the concrete parameter design scheme, the decision tree (DT) model performs best in predicting elastic energy, while the support vector machine (SVM) model performs best in predicting dissipative energy. For predicting bolt parameters, the DT model performs well in predicting elastic energy, while the Random Forest (RF) model performs well in predicting dissipative energy. Future research could enhance the model's performance by adjusting the model parameters, optimizing the fusion model, and utilizing ensemble learning to more accurately predict the performance indicators of design parameters of supporting structure. Meanwhile, it is important to recognize the limitations of this study. First, when establishing the model in this article, the influence of a single variable on the dissipation energy and elastic performance of the surrounding rock is studied by controlling it. In subsequent research, orthogonal experiments should be used to control multiple variables and explore the impact of multivariate changes on the dissipation energy and elastic performance of the surrounding rock. Second, there is a lack of empirical validation for the proxy model, particularly in terms of the prediction accuracy of ESE and PDE. Future research should focus more on introducing conditions that are closer to actual operating conditions. Although the proxy model exhibits good predictive capabilities in simulations, its applicability in practical engineering still requires further verification. To enhance its practicality, research could further consider the anisotropic characteristics under complex geological conditions, the dynamic changes in stress-strain during construction processes, and the nonlinear effects caused by environmental factors and material property fluctuations. Additionally, it is recommended to adopt a more diverse dataset and input parameters to conduct an in-depth analysis of the impact of support structure design on the energy evolution of surrounding rock in order to optimize algorithms and identify key factors affecting slope stability. In summary, although this study provides important insights into slope stability analysis, future research needs to address the aforementioned limitations to further refine the model and enhance its practicality and reliability.