A proposed topology identification method, based on a relational-graph convolutional network, addresses the challenges faced by traditional methods in adapting to the complex electrical coupling characteristics of low-voltage distribution substation areas with a high proportion of distributed photovoltaic (PV) integration. Firstly, the paper analyzes the influence mechanism of distributed PV integration on the identification of line-user relationships in low-voltage substation areas, and presents an adaptive method for identifying line-user relationships in distribution substation areas with high-penetration distributed PV integration. This method achieves line-user relationship identification through voltage Pearson correlation coefficient matrix modeling and global adaptive clustering. Secondly, considering the topology association characteristics of distribution substation areas with high renewables, the paper classifies and matches distribution network node associations into separated, hierarchical, parallel, and PV node acceptance relationships. This establishes an adjacent matrix model of substation area topology that is specifically adapted to distributed PV integration. Finally, the paper proposes a distribution substation area topology generation algorithm based on a relational-graph convolutional network. By extracting voltage measurement data to create node feature portraits for the substation area and employing graph link prediction to uncover potential node association relationships, the distribution substation area topology is progressively generated. A case simulation comparison validates the effectiveness of the proposed topology identification method, which improves identification accuracy by more than 4.3% when compared to traditional algorithms.