To address the problem that the public opinion data collection on food safety is not fast and accurate enough in the era of big data, this study proposes a public opinion monitoring probe on food safety based on the Bayesian network. Firstly, the MySQL database is used to establish a food safety keyword database. Secondly, the Bayesian network model is adopted to build a monitoring probe with the keyword database, and the public opinion monitoring system of the “Zhongyun Big Data” of PeopleYun is chosen for data collection. Thirdly, the monitoring probe is compared with traditional data collection technologies on public opinions and Web crawler technologies in three groups of comparative experiments (milk, wine, and tea) to verify its effectiveness. The results show that the data mining time of the three groups of experiments (milk: 3 s; alcohol: 2.5 s; tea: 2.4 s) is significantly reduced, and the data efficiency (milk: 83.6%, alcohol: 77%, tea: 77.9%) is considerably enhanced. Therefore, introducing a keyword database into the bayesian network model to form a monitoring probe can effectively improve the timeliness and accuracy of public opinion data collection on food safety.