## 5 Conclusions  
In this paper, a technical workflow is constructed to predict the production feasibility of the mining project and whether it will be put into the production stage according to the characteristics of the high feature dimension of the global gold mine project data dataset and the concentration of missing fields in the project data. The global gold mine project dataset is taken as the research object, and the workflow is completed based on microforest imputation and Random Forest algorithm to complete, including the construction of an index system, missing value imputation, model construction, and evaluation. Through the modeling and analysis of existing production projects in the world, the important influencing factors that determine whether gold mines are put into production are obtained, and the empirical analysis of actual projects is given. Combined with examples in the geological field, the algorithm process of imputation and interpretability evaluation is derived in detail, which provides theoretical support for geologists to deeply understand the mathematical principles of the microforest imputation algorithm and SHAP value. Through the research of this paper, the conclusions are mainly as follows:  
1. The accuracy rate in the prediction of the validation dataset imputed by microforest was obviously improved. After constructing a Random Forest model on the dataset imputed by microforest, the accuracy rate in the prediction was improved from 93.80% to 95.99%.
2. The Random Forest algorithm was used to construct the production feasibility model of the global gold mine project. It is verified by the accuracy, recall rate, and false positive rate that the model has high training accuracy and inspection accuracy, which proved the accuracy of the model construction, and there was no obvious overfitting scenario.
3. The estimated production life and ore deposit grade can be obtained in the feature importance ranking results of the SHAP value algorithm model as the most important factors affecting the production of gold mining projects.
4. This paper proposes a workflow for the evaluation of global gold mining projects' production feasibility, which achieves satisfactory application results and provides new ideas for mining project assessment research and mining development efficiency in the era of big data.  
Figure 12: Verification project (SHAP value force plot). (**a**) analysis of the possibility of project A’s production and its influencing factors; (**b**) analysis of the possibility of project B’s production and its influencing factors.  
**Author Contributions:** Conceptualization, K.K., Q.C. and K.W.; funding acquisition, Q.C.; methodology, K.K., K.W. and Y.Z.; visualization, K.K., X.R., C.S. and B.C.; writing--original draft, K.K., D.Z., G.Z. and J.X.; writing--review and editing, K.K. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese Academy of Engineering strategic research and consulting project (Grant 52922023002); the National Natural Science Foundation of China (Grants 42271281; 92062111); the China Geological Survey Program (Grants DD20230040; DD20221694; DD20211405; DD20190674).  
Not applicable.  
**Data Availability Statement:** 3 rd Party Data. Restrictions apply to the availability of these data. Data was obtained from S&P Global and are available [from the URL: [[https://www.capitaliq.spglobal.cn](https://www.capitaliq.spglobal.cn)]([https://www.capitaliq.spglobal.cn](https://www.capitaliq.spglobal.cn)), accessed on 1 March 2023]] with the permission of S&P Global.  
We are grateful to the National Natural Science Foundation of China and the China Geological Survey for their financial support.  
The authors declare no conflict of interest.