Foodborne diseases have a long history and cause huge social and economic losses every year. Artificial intelligence technology has brought new approaches to the detection and warning of foodborne disease events. Based on Internet big data, this study develops an intelligent detection and risk prediction platform for foodborne disease events. The platform is oriented to the data automatic acquisition, data analysis and visual display of foodborne disease events in the Internet, through D-M-V layered models and modules. The platform solves the problems of data acquisition, data fusion, event detection, risk prediction and visualization of foodborne disease events. The platform can automatically collect social media data, social economy data and other data from the Internet, make heterogeneous data efficient fusion according to the spatio-temporal coordinates of the data, detect foodborne disease events from social media data and infer their key information; use multi-source data to predict foodborne disease risks, and provide efficient visualization methods and interactive means. In this study, we use the 2018 Beijing foodborne disease data as an example to verify the platform function.