Parameter identification plays a crucial role in the monitoring and management of transmission systems. However, current technology faces the following limitations: (1) Traditional methods only focus on the data of a single branch, ignoring the information of adjacent branches in the overall power grid topology; (2) The data pollution caused by external factors, such as data loss and noise, has a negative impact on the accuracy of parameter identification. In response to the above challenges, this article constructs a graph neural network model with GraphSAGE as the main body. This model integrates noise filtering modules and multi task loss training strategies, fully utilizing the advantages of graph convolutional networks to achieve efficient identification of transmission system parameters. This model can not only capture the topology information of the power grid, achieve joint identification of multiple branches, but also effectively handle data loss and noise, improving the robustness of the model. The experimental results show that compared to traditional methods, our GraphSAGE model has achieved significant improvements in the accuracy and stability of line parameter identification.