Pricing Mechanism of Personal Data Trading Based on Multi‐Dimensional Dynamic Model

ABSTRACT Dynamic pricing has become a core approach for optimizing data value in both personal and enterprise contexts. However, accurately determining data prices across multiple dimensions remains a key research challenge. This study proposes a novel dynamic pricing mechanism based on a multi‐dimensional dynamic model to enhance pricing accuracy. The model analyzes pricing factors across various dimensions and dynamically adjusts prices according to real‐time data attributes and usage scenarios. Experimental results show that the proposed model achieves a pricing deviation of only 129 yuan from actual values (approximately 2.5%), significantly outperforming the traditional equal pricing model. The proposed model reduces the maximum error by 2.5 and the root mean square error by 2.2 in comparison. In addition, it demonstrates improved computational efficiency, with a runtime reduction of 296.10 milliseconds, and achieves an absolute increase of 14.29 percentage points in the F1 score and 14.90 percentage points in recall rate. These results indicate that the multi‐dimensional dynamic pricing model offers superior performance in both pricing precision and operational efficiency. The findings provide valuable insights for developing more accurate and adaptable data pricing strategies in real‐world applications.