Abstract: Abstract： The potential systemic risk in the financial industry is difficult to be accurately identified. Based on the loan data of the direct systemic risk contagion channel and internet text information of the indirect channel， a multi-layer bank-enterprise network is constructed， and a multi-layer bank-enterprise network convergence model is designed by using graph convolutional neural networks （GCN）. Based on the converged network， this paper quantitatively evaluates the systemic risk contagion process of 29 banks and 75 real estate institutions. The converged network analysis shows that the systemic risk transmission capacity under the joint impact of multi-layer bank-enterprise network is significantly greater than the systemic risk of single or two-layer network， and the systemic risk of the inter-enterprise network based on the indirect channel is more obvious. Financial prudential supervision should pay more attention to the ability of data analysis， deep learning and other technologies to integrate big data financial resources and effectively improve the level of risk monitoring and warning.

Key words: Key words： convergence of multi-layer network, systemic risk contagion, graph convolutional neural network, text analysis