Click through rate (CTR) on sponsored search ads determines the search engine’s revenue, thus analysis on users’ ads-clicking intent is one of the fundamental work to improve CTR. Based on the search logs provided by a Chinese search engine, this paper presents statistical analysis of ads clicks, and further proposes two methods to predict ads-clicking intent of query, namely query content match based prediction and Bayesian classification, respectively. Experimental results on large scale real data show the improvements from 3.0％ to 36.8％ in precision and from 0.060 to 0.408 in F-measure on sponsored search ads delivery. The proposed methods are capable of predicting the intent of user queries and enhancing the effect of search engine advertising, and are also applicable for online prediction of advertising click intent of user queries.