Large amounts of content with location and time tags are generated every day on webs such as microblog, news, and group-buying. Thus, it is important to find top-k results that satisfy users' temporal and spatial requirements from the contents. In this paper, a novel kNN query (called ST-kNN query) processing approach is proposed for content with location and time tags. First, location variables and time variables of data objects are transformed via temporal & spatial similarity in order to map data objects to a new three-dimensional space. Next, the spatial similarity between two objects in the three-dimensional space is used to approximate the actual temporal & spatial similarity. Then, a new index called ST-Rtree is designed in this three-dimensional space. The index combines location variables & time variables, and ensures every object is traversed no more than once. At last, an exact kNN query algorithm is proposed. The region is determined by computing only once to find top-k results, which guarantees high-efficiency in the query processing. Experiments on large datasets demonstrate that the presented query processing approach is very efficient.