Reducing the risk of enterprise capital chain breakage is the key to guarantee the stable operation of enterprises. This paper analyzes the characteristic variables affecting the financial status of enterprises and constructs the financial early warning index system. Use the isolated forest anomaly detection algorithm to calculate the likelihood of sample data anomalies, and quickly realize data preprocessing. Using K-neighborhood algorithm to complete the calculation of financial data distance, to determine the early warning classification of the sample enterprise indicator data. Combined with circular experiments to find the optimal parameters of the financial early warning model. Compare the prediction accuracy of the financial early warning model of each classification to verify the advantages of the model in this paper. The results show that the model has the highest prediction accuracy when the environmental parameter b takes the value of [0.2,0.9] and the threshold percentage p takes the value of [0.7,1]. The prediction accuracy of the financial early warning model based on the K-neighborhood algorithm reaches 84.94%, which is higher than that of the other nine prediction models, and it has excellent financial risk prediction capability.
