Electrocardiography (ECG) is a commonly used method for detecting heart conditions, and its fiducial points can be used to diagnose various cardiovascular diseases. Many studies utilize deep learning models to assist with annotation; however, due to individual differences and noise interference, generating labeled data for model training is time-consuming and labor-intensive. This study discusses the training of deep learning models based on data. It explores the impact of using model-automated annotated data as training data on the results of ECG annotation. This study selected the Residual U-Net (R-Unet) as the experiment's model architecture. After training with manually labeled data, we use model-labeled data to increase the data volume and perform progressive learning. The results showed that the accuracy of the model trained with partially non-manually labeled data was comparable to that of the model trained with manually labeled data. Finally, the study demonstrates that through progressive learning, it is possible to maintain the model's accuracy while reducing the workload of generating training data by utilizing unlabeled ECG data.