Abstract: Rumors spread can destroy social order, endanger national stability and cause public panic. The wide application of social platforms makes information spread faster and more widely, increasing the negative impact caused by rumors. How to quickly and accurately identify online rumors has become a hot issue in the field of information dissemination. Rumor recognition is a binary classification problem. Therefore, based on the idea of Bayesian classification, a Naive Bayesian classification algorithm for network rumor recognition is designed. The naive Bayesian classifier is constructed by Matlab software, and the algorithm is verified by experiments with data collected from microblogs. By controlling the training set, the accuracy, precision, recall rate and F1 value of the identification results are compared, and the identification situation and inherent laws of the naive Bayesian classifier for rumor and non-rumor under different training conditions are explored. The research shows that naive Bayesian classifier is effective for online rumor identification, the selection and control of training sets have great influence on the identification results, and the identification accuracy fluctuates with different training conditions. 

Key words: Naive Bayesian classification, rumor identification, machine learning