The development of cloud computing and big data analysis has given rise to various disaster prediction methods. To reduce the probability of fire accidents, it is critical to predict the fire risk by mining the massive historical data on fire. Considering the advantages of MapReduce, a cloud computing method, in parallel processing of data, this paper puts forward a novel prediction method for fire risk that mines the association rules in the time domain. Firstly, the risk of disaster-causing factors and the ability of disaster-reducing factors were evaluated. Based on the evaluation results, an evaluation index system was constructed for fire risk, and the indices were quantified through proper weighting. Facing the historical fire data, the authors designed the spatiotemporal density-based spatial clustering of applications with noise (spatiotemporal DBSCAN), and quantitatively evaluated fire risk by the association rule mining algorithm based on time domain partition (TDP). The effectiveness of our method in fire risk prediction was verified through experiments. The research results provide reference for the risk prediction of other disasters.