In order to improve the efficiency of substation inspection, a detection and recognition method of distorted terminal logo text in substation based on deep learning was proposed. The terminal identification detection model uses DBNet as the basic framework, replaces the original ResNet backbone network with ConvNeXt V2, and uses its modern architecture design to significantly improve the global information modeling ability of the model and the feature extraction ability of the terminal identification. In order to further improve the detection accuracy of distorted terminal signs, an efficient multi-scale attention module EMA and a deformable convolutional network DCNv4 are introduced to effectively improve the ability to capture global context information and enhance the robustness to irregular text shapes. After optimization, the detection accuracy of the terminal sign detection model reaches 97.4%, which is 18.9 percentage points higher than the original model. And the amount of calculation is only increased by 2.9%. The terminal identification model takes CRNN as the basic framework, and introduces spatial and channel convolution SCConv to optimize the feature extraction process, which significantly reduces redundant features and reduces the computational burden. In the sequence modeling part, the selective state space network Mamba is used to replace the original LSTM, its state space model and selective mechanism can dynamically model the sequence data, adaptively focus on the important part of the sequence, and significantly enhance the ability to capture long sequence dependencies. The recognition accuracy of the optimized terminal identification model reaches 98.2%. It is 2.7 percentage points higher than the original model. Experimental results show that the proposed method has excellent detection ability and recognition accuracy for negative factors such as distortion, weak light and blur in substation terminal identification.