With the explosive development of mobile applications, users can find a tremendous amount of travel information with just a few phone taps. However, from the perspective of information quality, most applications lack a well-organized structure that can fit various user needs. On the other hand, it is also important to consider the cultural and historical characteristics of the visited sites, in addition to focusing on sightseeing. The reason is that there are often some specific relationships among the sites. For example, many temples in Taiwan are branched from a root temple. Thus, the presented information should not be limited to a single site at a single time point. A mechanism that can help users to get the desired information more effectively and efficiently is preferred. In this paper, we are going to present an intelligent and context-aware touring system by developing an active recommendation mechanism and application. We will adopt ontology to construct the temporal, spatial, and causality relationships among the tourist attractions. Then, a personalized recommendation mechanism will be developed by integrating context-aware and data mining techniques to make recommendation decisions and “push” the information to the user’s mobile devices. In this way, users can get the most appropriate information while sightseeing.