With the acceleration of digital transformation, the decision-making environment of enterprises is becoming increasingly complex. Market fluctuations, policy adjustments, and other factors lead to increased decision-making risks. Traditional risk warning methods are not suitable for the needs of modern enterprise management due to their reliance on manual analysis and delayed response. The current management decision-making risk warning system is facing core issues such as inaccurate data processing, delayed risk identification and lack of intelligent support for prevention strategies, hindering the scientific and safe decision-making of the company. The structure and content of this article: Firstly, it identifies the core types and influencing factors of enterprise management decision-making risks, and constructs a multidimensional management risk indicator system. Secondly, it has developed a risk warning model based on deep learning, integrating LSTM and attention mechanisms to deeply analyze time series decision data and predict risk trends. Finally, it proposes an intelligent avoidance strategy generation algorithm that adapts to the output of the model. The experimental results show that the proposed model has an accuracy of 92.4%, which is 5.7 percentage points higher than the sub optimized LSTM model, verifying the efficiency and practicality of the model and algorithm.
Dongmei Wang (Thu,) studied this question.