Anomaly detection of Key Performance Indicators (KPIs) e.g. response latency, network throughput, etc., is one of the key techniques to ensure the quality and security of network services. However, state-of-the-art unsupervised deep learning algorithms present limitations: they are sensitive to noise and demand extensive KPI data for training, complicating the detection process. This paper proposes an Unsupervised Deep Transfer Learning (UDTL) approach. First, UDTL uses self-training preprocessing that generates reliable, high-quality samples for model training. Then, UDTL calculates the correlation based on the shape of the KPIs and the deviation scores of the KPIs, and clusters these KPIs into different clusters via correlation. At last, UDTL selects the KPI closest to each cluster’s centroid to train a base anomaly detection model for this cluster. The anomaly detection model of other KPIs adaptively choose the parameters to transfer based on correlation. Our experiments conducted on several public datasets highlight UDTL’s effectiveness. UDTL enhances the F1 score by 15.24% and reduces the training time by 22.17 times compared to baselines.