In this paper, we utilize big data to screen relevant data on charging safety influencing factors and perform data cleaning to constitute a charging safety influencing factors dataset. BERT is selected as the baseline model for the named entity recognition task, together with the CRF model, to exclude irrelevant features, resulting in an effective model for entity recognition in line with the knowledge graph. Introducing a security database, a graph attention network model that simultaneously obtains the structural features and textual description features of the security knowledge graph is proposed to improve the performance of knowledge graph relationship extraction. The dataset of high-frequency charging security composition, as well as the random dataset, are used as experimental samples, respectively, to compare and analyze the performance of the BERT-CRF named entity recognition model in terms of each index. The link prediction evaluation task is evaluated using the structure- and text-based graph attention network model, and experimental analysis is carried out using three benchmark models. From the overall results of the test, it can be seen that the BERT-CRF model learns 90% of the lexicon’s knowledge and passes the model test by keeping each evaluation metric in the range of 0.9 to 1.0 under the large data volume experimental environment. The proposed graph attention network model, which uses structure and text, has a better link prediction performance than other models and performs better in the FB15K-237 dataset.