With the rapid development of Internet, the traditional personalized recommendation involving only users and projects cannot meet demands in efficiency and accuracy of the recommendation. Therefore, context-aware recommendation has drawn wide attention and become a new research hotspot. This paper analyzes the definition of context and the model of context-aware recommendation. It also proposes an association rule recommendation model based on context information which reduces the number of dimensions. The data source of experiments is web log. Finally, this paper combines the temporal context and implements the association rule recommendation algorithm based on the temporal context partition. Compared with the traditional recommendation algorithm, the results of experiments show that the context-aware recommendation algorithm has higher accuracy and recall rate.