With the development of machine learning, building electrical system diagnosis is gradually becoming automated. However, traditional methods require tedious feature engineering, which greatly limits the speed and accuracy of diagnosis. In response to the above problems, a deep learning-based diagnostic method for building electrical systems is proposed, which uses the unique feature self-learning ability of deep learning to achieve the purpose of diagnosing complex building electrical systems, and in-depth research and analysis have been carried out.