The accuracy of seismic horizon identification results significantly impacts the precision of subsequent structural interpretation and reservoir analysis, making it a critical step in seismic data interpretation. While deep learning achieves horizon identification by establishing a mapping relationship between seismic data and training labels, networks often struggle to learn effectively when faced with highly similar profile features within the same survey area, leading to issues such as discontinuities and horizon mis-ties. To fully leverage the spatial relationships between seismic horizons and enhance network feature extraction capabilities, this paper proposes a progressive methodology based on the VGG16-UNet architecture. This paper first employs data augmentation to enrich seismic profile features and then introduces the Spatial and Channel Squeeze and Excitation (scSE) attention mechanism to improve the representation of salient features, designing the scSE-VGG16-UNet architecture. Building on this foundation, the deep supervision mechanism from UNet++ is integrated to finally propose the data-augmented scSE-VGG16-UNet++ method for seismic horizon identification. Quantitatively, the baseline VGG16-UNet method achieves a Pixel Accuracy (PA) and mean Intersection over Union (mIoU) of only 93.01% and 46.96%, respectively. Through the synergistic effect of data augmentation, the scSE attention mechanism, and the UNet++ architecture, the final data-augmented scSE-VGG16-UNet++ method elevates these metrics to 97.70% and 79.23%. The test results on both model data and field data demonstrate that the proposed method not only achieves high-continuity horizon identification and effectively mitigates the problem of horizon mis-ties, but also exhibits robust performance in the presence of noise, indicating superior generalization ability.