Abstract: With the deep development of the economic society,and the remarkable rise in people's living standards, customer requirements are becoming more and more intense. Meanwhile, the rapid development of the Internet technology and smart manufacturing has provided a solid industrial foundation for meeting such needs. Customized production has gradually become an important production mode. Different from traditional massive production, in a customized production mode, a product often consists of multiple customized modules, generating thousands of customized combinations, and usually a small number of products will be produced for each combination. Taking certification cost into account, the customized production makes the traditional product certification procedure unrealistic because we can not authenticate several prototypes for each customized combination. Therefore, there is an urgent requirement for developing theories and methods of customized product certification, which can accurately assess the quality of customized products at an acceptable certification cost. In this article, we propose a general framework for customized product certification based on certification big data and statistical models and illustrate it on refrigerator safety certification. This framework transforms the safety certification of customized products into the assessment of product safety risks, establishes a quantitative statistical model to characterize the risks, and uses the principles and methods of experimental design to develop economical certification schemes. Our certification big data based simulation results indicate that this framework has the potential to achieve reliable, efficient, and intelligent customized product certification, which has important theoretical and practical significance for certification mode innovation.