[Objective] In the context of data transaction, in order to strengthen data circulation management and improve data circulation transaction rules, a set of comprehensive data quality management system and technical framework under the scenario of data circulation transaction are constructed according to the focus of data product quality evaluation and management. [Methods] Using literature research method, we reviewed the current literature of data quality assessment and commonly used methods of data quality inspection at home and abroad. Combining industry experience and specific scenarios of data transactions, we proposed a quality evaluation model containing raw data sets, desensitized data sets, modeled data, and AI-based data, along with a management system to improve the data quality before, during, and after data transactions. [Results] This paper raises a data quality evaluation model in transaction context that based on the “6543” structure, namely six types of main indicators, five types of subjects, four types of products, and three types of evaluation methods. Provide testing and optimization solutions to data normativeness and completeness in the pre-transaction phase, data accuracy and consistency during the transaction phase, as well as data timeliness and accessibility in post-transaction phase. [Limitations] The data quality model and management system have not been systematically used in real transaction scenarios, and there is a lack of actual testing. [Conclusions] The proposed quality evaluation model and quality management system play an important role in realizing the quality evaluation and improvement of data products in the whole process of data transaction.