Current popular prediction methods of foreign trade product sales only respectively study prediction problems from angles of the third party platform or big data, lacking consideration of dynamic evolution prediction on product sales based on Internet platform, big data and cross-border e-commerce. To improve the efficiency of export sales prediction, to achieve scalability and dynamic evolution of prediction systems, with mining controllable relevance big data of cross-border e-commerce export sale based on "Internet+foreign trade" surroundings, personalized prediction mechanism and smart prediction algorithms, improving corresponding algorithms such as distributed quantitative calculation and centralized qualitative calculation, a dynamic prediction model on export sales based on "Internet+foreign trade"-driven controllable relevance big data of cross-border e-commerce was proposed. Finally, this model was verified and analyzed. The performance analysis results show that the model integrates fully openness and extensibility of "Internet+" and dynamic prediction advantages of big data, achieving dynamic, smart, quantitative, and qualitative prediction on export sales based on "Internet+foreign trade"-driven controllable relevance big data of cross-border e-commerce. The comprehensive prediction efficiency of the proposed model is obviously better than those of traditional models, and it has stronger dynamic evolution and higher utility.