The existing multi-source transfer learning algorithms have very few researches on regression problems, and most of them are symmetric two?class classification problems. This paper presents a weighted multi?source TrAdaBoost regression algorithm, in which the error tolerance coefficient is proposed to solve the problem that the sample weight of the source domain is reduced too quickly, thus the effect of the algorithm is improved. Experiments are performed on the modified Friedman #1 regression problem to verify the effectiveness of the algorithm. The error tolerance coefficient can increase the score by approximately 0.01. In this paper, the proposed algorithm is applied to the industry problems of power communication networks, and the anomaly site (sites with a large number of missing services) detection and true value prediction models are proposed. Moreover, the social network analysis methods are used in the feature engineering, and the importance of the site in the topology is fully considered. Finally, experimental results verify the effectiveness of the algorithm.