Wind turbine clutter (WTC) can severely degrade the quality of meteorological radar data, particularly under complex weather conditions. Because WTC echoes can substantially overlap with precipitation echoes, they may introduce considerable errors into weather identification and forecasting. To address this issue, a deep learning model termed WTC-CNXGRN is proposed for automatic WTC detection. The model is built on the ConvNeXtV2 architecture and is designed to address characteristic WTC features, including unstable spatial structure, pronounced local texture variations, and complex multi-channel feature coupling. Multi-parameter dual-polarization weather radar data are used as input, while local statistical features and weighted polarimetric features are incorporated to strengthen the representation of spatial echo variations. In addition, a Global Response Normalization (GRN) mechanism is integrated into the network to adaptively recalibrate channel-wise responses, thereby enhancing the representation of critical discriminative features. On the test dataset, the proposed model achieves an accuracy of 98.74%, a precision of 98.41%, a probability of detection (POD) of 98.34%, and a false alarm ratio (FAR) of 1.59%, achieving overall better performance than the evaluated baseline models. Benefiting from its efficient architecture and improved detection strategy, the model provides low computational cost and fast inference, making it suitable for near-real-time operational applications. Qualitative tests using radar data from the Nantong region further provide preliminary evidence of the model’s cross-site applicability.