An Improved and Combined Model for Data Pricing Based on Automated Machine Learning and Knowledge Graph

Aiming at the problems of the current data pricing model, such as the difficulty of evaluation, the lack of scientific and unified rules for data transactions, the lack of privacy protection of data, and the poor accuracy of pricing, this dissertation proposes an improved data pricing combination model, which firstly establishes a feature recommendation model based on the knowledge graph, selects the optimal feature combinations, and improves the automated machine learning algorithms to obtain the optimal machine learning regression model for data pricing. Finally, the case study demonstrates that the method proposed in this paper has good applicability and accuracy