This research explores the use of LSTM (Long Short-Term Memory) neural networks for forecasting systemic financial risks and introduces an innovative risk prediction model built on the LSTM framework. In terms of methods, this article first summarizes the systematic financial risks, and describes in detail the process of data preprocessing, including data cleaning, normalization, missing value processing and so on. Then this article designs a risk prediction model based on LSTM, and optimizes the model structure by introducing dropout layer and batch normalization layer. In the experimental verification stage, the model is trained by cross-validation, and several assessment indexes such as accuracy, recall and F1 score are selected to quantify the prediction performance of the model. The LSTM risk prediction model performs well in all assessment indexes. It shows strong stability and accuracy in long-term risk prediction, which is obviously better than the Value at Risk model. This outcome confirms the practicality and success of using the LSTM network for predicting financial risks in a systematic manner. This study enriches the theoretical system of financial risk prediction and provides strong decision support for financial supervision departments and financial institutions.