To address the challenge of low hit rate in thunderstorm cloud identification, deep learning techniques are employed to enhance recognition accuracy. The research focuses on developing a novel thunderstorm cloud identification model, LkaUNet, which integrates a large kernel attention (LKA) mechanism with the U-Net architecture. This design enhances the model’s ability to capture global morphological features and long-range spatial dependencies of thunderstorm clouds. The study utilizes S-band radar base data and lightning observation data from Hunan Province (2022~2023), employing multi-stage quality control to synchronize radar composite reflectivity and lightning data while suppressing noise. This process generates high-quality datasets of lightning probability and radar composite reflectivity mosaics as input. The LkaUNet model builds upon the U-Net framework and incorporates large kernel attention modules to expand the receptive field, thereby improving long-range dependency modeling and feature perception. Experimental results demonstrate: When trained with regression loss functions and a threshold exceeding 0.4, the LkaUNet achieves higher critical success index (CSI) and negative alarm probability (NAP), along with a lower false alarm rate (FAR), compared to the baseline U-Net model; Under classification-based loss training, LkaUNet achieved a CSI of 0.730 1, with corresponding detection metrics of 86.27% hit rate, 13.73% miss rate, and 18.45% false alarm rate. The study concludes that LkaUNet effectively models long-range spatial correlations in thunderstorm clouds, providing a robust deep learning solution for monitoring severe convective weather. The approach highlights the potential of attention mechanisms in meteorological applications.