It was demonstrated that chain equation Multiple Imputation is the more accurate model for data imputation because it conditionally models the missing variables of other variables [23]. Microforest is a type of multiple imputation by chained equations algorithm that employs an ensemble of decision trees to improve missing data processing for machine learning and deep learning applications [24; 25; 26]. Microforest utilizes predictive mean matching (PMM) to iteratively update missing values by comparing the estimate to other observed values in the dataset and selecting the value of the nearest neighbor to estimate lost data. This process enables multiple imputation by chained equations algorithms to improve performance by using decision tree-based regressors to impute missing values for categorical and binary variables in datasets [27; 28]. Compared to other multiple interpolations and deep learning interpolation methods, Microforest implements machine learning chain equation interpolation, which has fast and high memory utilization characteristics and can insert missing classification and regression data without too many settings; a user can also customize every aspect of the imputation process and output a series of available diagnostic maps, providing an effective way to evaluate the results [25; 27]. Recent studies have demonstrated the effectiveness of multiple imputations by chained equations algorithms in imputing missing geological datasets with continuous variables. Additionally, these methods have been successfully utilized to impute missing values in the field of bioinformation and medical big data [29; 30]. In addition, machine learning modeling is usually difficult to scientifically explain results due to the characteristics of the black box. Traditional feature importance metrics only indicate which features are important in predicting outcomes but do not provide a clear understanding of how these features affect the prediction results. Shapley Additive explanation (SHAP) value is a way to address the model interpretability, based on Shapley values, to determine the importance of that individual by calculating the contribution of the individual in the cooperation [31]. SHAP has been applied in multiple fields of research and has achieved good results [32]. [PERSON] (2022) uses the SHAP value to select the first four features of the optimal model to study how each feature affects the wastewater parameters and finally improve the effluent quality control strategy [33]. [PERSON] (2022) used a dataset composed of hundreds of jet engines to reveal the correlation of monotony, trend, and predictability among various features [34]. Previous big data research has mainly focused on mineral potential assessment, but there has been less research on the evaluation of mining projects themselves. At present, the feasibility study of mining projects is mainly conducted through manual due diligence, which requires a lot of manpower and material resources, and focuses on qualitative analysis, lacking quantitative comparison of similar projects using big data methods. Since data from mining projects have the characteristics of high dimension and concentrated distribution of missing fields, this paper constructs a technical workflow including data filling, evaluation model construction, and production feasibility evaluation. The dataset is established by the Miceforest imputation method. An evaluation model is based on a Random Forest algorithm to predict the possibility of the project entering the production stage and finally utilizes the SHAP value method to evaluate the interpretability of the factors affecting production. The workflow, from dataset construction to rapid project evaluation, will provide a reference for gold mine production feasibility assessment. It can have a certain significance for the mining development efficiency of gold mines.