To address the issue of insufficient intelligent analysis and autonomous decision-making capabilities as well as low fault diagnosis efficiency in building equipment operation and maintenance processes， a novel fault diagnosis method based on knowledge graphs and multi-task learning was proposed. Firstly， an operation and maintenance-oriented knowledge graph was constructed， and multi-source heterogeneous data were extracted from building equipment systems by using natural language processing and entity linking techniques， thereby obtaining rich knowledge representation. Secondly， in the case of few-shot labeling， multi-source symptom associated identification was explored. And unlabeled data were used to optimize the model parameters iteratively through self-training and co-training strategies， so as to improve generalization capability of the model. Finally， when designing fault root cause localization technology based on deep knowledge reasoning， a probabilistic graphical model was utilized to trace the fault propagation paths in complex equipment systems， thereby enhancing the accuracy and interpretability of fault analysis. Simultaneously， fusion mechanism was introduced into multi-task learning framework， thereby improving performance of the proposed method on fault diagnosis tasks. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 92% with an average diagnosis time of 6.5 seconds per case， outperforming the comparison models in evaluation metrics such as accuracy， precision， and recall.