To improve substation inspection efficiency,this paper proposes a deep learning-based method for detecting and recognizing distorted terminal block labels in substation.The detection model is built upon the DBNet framework,wherein the original ResNet backbone is replaced with ConvNeXt V2.This upgrade uses ConvNeXt V2's modern architectural design to significantly enhance the model's global information modeling and feature extraction capabilities for terminal identification.To further improve the detection accuracy for twisted and deformed terminal labels,we integrate an Efficient Multi-scale Attention (EMA) module and a Deformable Convolutional Network (DCNv4) to effectively strengthen the model's ability to capture global contextual information and enhance its robustness to irregular text shapes.After optimization,the terminal detection model achieves F1 score of 97.4%,which represents an 18.9 percentage-point improvement over the original model,with only a 2.9% increase in calculation amount.For the recognition model,we take the Convolutional Recurrent Neural Network (CRNN) framework and introduce Spatial and Channel Convolution (SCConv) to optimize the feature extraction process,which significantly reduces redundant features and alleviates the computational burden.In the sequence modeling part,the selective state space network Mamba is used to replace the original Long Short-Term Memory (LSTM).Mamba's state space model and selective mechanism enable dynamic sequence data modeling,allowing it to adaptively focus on critical parts of the sequence and significantly enhance the capture of long sequence dependencies.The optimized recognition model achieves an accuracy of 98.2%,which is 2.7 percentage points higher than the original model.Experimental results show that the proposed method exhibits excellent detection capability and recognition accuracy for substation terminal identification under challenging conditions such as distortion,weak light and blur.