In order to improve the accuracy of enterprise financial risk early warning and realize the risk control of enterprise capital chain under digital transformation, this paper adopts the Long Short-Term Memory (LSTM) neural network algorithm to establish the enterprise financial risk early warning model. First analyze the enterprise financial risk early warning indicators, use factor analysis for indicator screening, determine the indicator weights through the Delphi method and the improved hierarchical analysis method, and select the indicators with high importance to construct the enterprise financial risk early warning feature samples. Then after LSTM neural network training, the enterprise financial risk early warning model is obtained, and the model is evaluated for performance and practical use. The experiment proves that the accuracy of the LSTM neural network model on the training set and test set is 91.48% and 88.62% respectively, which indicates that the model can effectively predict the enterprise financial risk. By comparing with the commonly used enterprise financial risk warning algorithms, the algorithm in this paper has the highest warning accuracy, shorter prediction time, and better warning performance in dealing with large-scale enterprise samples. This study provides an effective financial risk early warning method for enterprises, which can help them better carry out capital chain wind control in the process of digital transformation.