Abstract: With the rapid development of data marketization,data pricing has become a critical issue that needs to be addressed.To address the opaque and inadequate interpretability of pricing mechanisms in this process,this paper proposes a data pricing model based on the grey wolf algorithm(GWO)optimized for XGBoost.Firstly,a raw dataset is obtained from the Youyi Data platform and subjected to descriptive statistical analysis.The data is then preprocessed by removing outliers,one-hot encoding,logarithmic transformation,and normalization.Feature correlation is analyzed using the Spearman correlation coefficient.Finally,the GWO algorithm is used to optimize XGBoost hyperparameters to improve the model's predictive performance.Experimental results indicate that the GWO-XGBoost model achieves a coefficient of determination(R²)of 0.971,significantly outperforming five baseline models.The GWO-XGBoost model also achieves significant improvements in metrics such as mean squared error(MSE),root mean squared error(RMSE),and mean absolute error(MAE)compared to traditional hyperparameter optimization methods such as grid search and random search.Furthermore,using the SHAP interpretability analysis method,an in-depthana-lysis is conducted of the model's prediction results from both global and local perspectives,identifying the data update interval as the dominant factor influencing the model's prediction results,contributing 95.16% of the total prediction increment.This research not only provides a scientific and rational mechanism for data pricing but also provides a clear direction for subsequent model optimization,which is of great significance for promoting the healthy development of the data element market.

Key words: Data pricing, Grey wolf algorithm, XGBoost algorithm, SHAP analysis