To enhance the operational management efficiency of the power communication network, artificial intelligence technology was utilized to develop a digitalization method for distribution panels within the communication power system. By implementing object detection and text recognition, real-time monitoring of the power supply status for each subordinate branch in the distribution panel was achieved. Firstly, a multi-layer nested recognition network (MLNRN) architecture was proposed, which incorporated lightweight strategies to reduce the computational demands on terminal devices, allowing for efficient and accurate structured output of the distribution panel's power supply status. Secondly, an improved YOLOv5 network was introduced for the task of icon detection. By integrating ConvNext Block and bidirectional feature pynamid network (Bi-FPN) structures, the recognition accuracy for small targets, such as status lights, was significantly enhanced. Finally, a text recognition model targeting the labels of subordinate branches in the distribution panel was constructed on the basis of the convolutional recurrent neural network-connectionist temporal classification (CRNN-CTC). Transfer learning and image augmentation strategies were employed to improve recognition accuracy for text with multiple character types in non-standard distribution panel images. Simulation experiments demonstrate that the average accuracy of image recognition is 97.2%, whereas the accuracy of text recognition is 92%. These results validate the effectiveness and applicability of the proposed architecture in the digitalization of power distribution panel, which provides an effective solution for video inspection and intelligent maintenance in power communication networks
