In recent years, with the complexity of the global financial system and the enhancement of the market risk communication characteristics, scientific construction of a mechanism that can effectively identify and dynamically monitor the potential risks of the financial system has become the focus of academic circles and policy makers. Therefore, this paper proposes a risk analysis framework based on deep learning technology. The framework achieves comprehensive assessment and high-precision prediction of systemic risks through the integration of multi-dimensional data and dynamic modeling. Meanwhile, it innovatively improves the flexibility and accuracy of risk monitoring by replacing traditional fixed indicators with adaptive parameters. Combined with the powerful nonlinear modeling ability of the deep learning model, multiple factors such as macroeconomic fluctuations and market behavior characteristics are effectively integrated into the risk assessment process. This paper verifies the relevant data of the financial market, and the results show that this method can not only capture the complex characteristics of market risks more accurately, but also adapt to nonlinear and dynamic environmental changes. It can also accurately rank the systemic risk level of different industries. The research results further reveal that the risk level of some industries is significantly higher than that of other industries, providing an important reference for policy formulation and industry supervision. In summary, the methods proposed in this paper have important theoretical value and extensive practical application significance in improving the efficiency of financial risk monitoring and stabilizing market operation.