The invention discloses a webpage junk detection method based on a dynamic Bayesian model, which relates to a method for detecting a cheating webpage. The webpage junk detection method mainly uses an improved dynamic Bayesian network model for modeling for click actions of users, and judges and identifies the cheating webpage; and a search engine query log records interactive information of the users and a search engine, wherein the content of the interactive information comprises the information including query terms, websites returned by the search engine, websites clicked by the users, timestamp and the like. Information including the clicked websites, a clicking order thereof and the like in the log reflects user preference. The webpage junk detection method models for the log click actions, and excavates a clicking causal relationship between the websites in a list sequence returned back by the search engine, thereby explaining which websites are considered to be associated with the query terms from the view of the users, and obtaining the relativity between the websites and the query from the view of the users; and the relativity is a connotative feedback, so that the cheating website is ranked low, and related websites are ranked higher.