Construction and Evaluation of Enterprise Operation Risk Early Warning Model Based on Decision Tree Algorithm and Electric Power Big Data

In the current era, electric power enterprises are facing a lot of corporate risks. How to detect and warn the operational risks of enterprises in a timely manner has become an important issue that electric power enterprise managers must face. At the same time, the continuous accumulation and application of electric power big data provides new opportunities and challenges for the establishment of effective risk early warning models. Based on the decision tree algorithm and electric power big data, this research builds an enterprise operation risk early warning model, and evaluates and verifies the model. Specifically, this study first uses the big data of electric power to extract the key indicators and characteristics of electric power enterprises, and then uses the decision tree algorithm to construct a risk early warning model based on feature selection and classification prediction. Based on the actual data of electric power enterprises in city A, this research builds an enterprise operation risk early warning model, and conducts model evaluation and verification. The results show that the accuracy rate, recall rate and F1 value of the model in this paper are higher than those of other methods, and the accuracy rate of the model in this paper is 61.6%, 85.6%, 89.3% and 91.6% in four different training times. The model proposed in this paper can effectively predict the operational risk status of electric power enterprises, and has high accuracy and stability. At the same time, the model can also provide good decision support and risk control strategies for business managers.