To improve the seal detection efficiency of threaded oil casing gas, this study proposes an automatic classification network, NAFENet, for threaded torque curves based on global attention feature fusion. Specifically, NAFENet extends the convolutional structure of EfficientNet-B0 to 11 layers to obtain EfficientNet-B11 and enhance the model expressiveness. Meanwhile, the modules based on non-local global attention and attentional feature fusion (AFF) are built in each MBConv convolutional layer to help the model acquire more global information in the curve images and improve the feature extraction ability. The experimental results show that compared with EfficientNet-B0, the parameter number of NAFENet is slightly increased with improved curve identification accuracy, and the model accuracy reaches 92.87% on the homemade UBT_Curve dataset.