[28] combined finite element simulation with machine learning, obtained meso mechanical properties through simulation, and put them into machine learning algorithm for training. Taking uniaxial elastic modulus, Poisson's ratio, tensile strength, compressive strength, and confining pressure of rock as characteristic variables, they carried out multi-objective machine learning on triaxial elastic modulus, Poisson's ratio, and compressive strength of rock. [PERSON] et al. [29] simulated the slope through FLAC to obtain the parameters, and used machine learning to develop the agent model. Table 2 shows the application of machine learning in the field of geotechnical engineering. In summary, although scholars have extensively studied the energy evolution characteristics (EEC) of surrounding rock and explored the effects of support systems on roadway stability, the specific mechanisms by which different support parameters influence the EEC remain inadequately investigated. Furthermore, while artificial intelligence (AI) and machine learning (ML) techniques have been applied in geotechnical engineering, there has been no systematic research utilizing machine learning methods to predict the impact of various support parameters on the EEC. To effectively address these challenges, this study takes the deep mining area of the Sanshandao Gold Mine as its research context. The primary objectives are to analyze the mechanisms through which different support parameters affect the EEC of the surrounding rock, optimize roadway support design from an energy perspective, and provide theoretical guidance. Additionally, five commonly used regression models (Back propagation (BP) [30], Decision Tree (DT) [31], Extreme Learning Machine (ELM) [32], Random Forests (RF) [33], Support Vector Machines (SVM) [34]) are introduced to investigate the application of machine learning in predicting roadway stability and evaluate the predictive performance of different models for the EEC influenced by various support parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Direction** & **Authors and References** & **Key Findings** \\ \hline Energy Dissipation Characteristics & [PERSON] et al. [18] & Investigated the stability of elastic-plastic and rheological states of rock masses. \\ Macro-Micro Coupled Models & [PERSON] et al. [19] & Developed a macro-meso-micro coupled model for damage evolution and energy release. \\ Acoustic Emission Characteristics & [PERSON] et al. [20] & Analyzed failure characteristics of roadway rockburst through acoustic emission. \\ Numerical Simulation and Energy Evolution Laws & [PERSON] et al. [21] & Used particle flow methods to reveal stress field evolution in surrounding rock. \\ Damage Models under Dynamic and Static Loading & [PERSON] et al. [22; 23] & Established a damage variable model through dynamic loading experiments. \\ Energy Transfer and Zonal Energy Models & [PERSON] et al. [24] & Proposed zonal energy storage models for surrounding rock. \\ Elastic Energy Transfer and Dissipation & [PERSON] et al. [25] & Proposed an energy dissipation zoning pattern considering strain-softening and expansion properties. \\ Dynamic Disturbances in Rockbursts & [PERSON] et al. [26; 27] & Explored effects of in situ stress, joint angles, and spacing on deep roadway rockbursts. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Research on Energy Evolution Characteristics (EEC) of Surrounding Rock. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Time** & **Model** & **Data Number** & **Research Object** \\ \hline [PERSON] et al. [28] & 2023 & ANN, SVR, RF & 384 & Mechanical property \\ [PERSON] et al. [29] & 2023 & DNN & 880 & Slope stability \\ [PERSON] et al. [30] & 2024 & ANN & 30 & Shear strength \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Applications of machine learning.