The bearing temperature of the blower is an important indicator to evaluate its stable operation. However, since bearings are usually installed in a relatively closed environment, it is difficult to achieve real-time and accurate detection of bearing temperature. To address this issue, a knowledge graph-based intelligent prediction of the bearing temperature of blowers is presented. First, a statistical method is applied to analyze the operational system of blowers, and the influencing factors related to bearing temperature are obtained. Second, a knowledge graph is constructed by combining mechanism and domain knowledge. In addition, the direct and indirect feature variables that affect the bearing temperature are extracted. Third, a dual modular fuzzy neural network is designed?to deduce the knowledge graph, and the real-time and accurate prediction of the bearing temperature of blowers is realized. Finally, the results show that the intelligent prediction method of bearing temperatures of blowers based on a knowledge graph can accurately model the blower system and has good temperature prediction ability. This research can provide support for real-time monitoring and change trend prediction of bearing temperatures.