With the growing concern of society to sustainable development

power grid green infrastructure file management has become an important driver for the power grid to achieve energy conservation

carbon reduction and digital development goals. Among them

the intelligent identification of file management is a key step to realize the above goals. As for the intelligent identification of power grid infrastructure archives collected offline

an improved long short-term memory (LSTM)-connectionist temporal classification (CTC)-based method for intelligent recognition of electronic power grid infrastructure files is proposed in this paper. The proposed method firstly extracts image data of the power grid infrastructure files

then inputs the images into the LSTM network to extract the sequential features of the text in the power grid infrastructure file image

and finally utilizes the CTC loss function calculation to obtain the final text identification results. At the same time

the weight update methods proposed in this paper are used to improve the CTC loss function

thus enhancing the model's identification generalization capability. The simulation results show that intelligent identification method of power grid infrastructure file based on the improved LSTM-CTC

which is proposed in this paper

can significantly improve the identification efficiency and accuracy of power grid infrastructure files.