Large group activity security risk assessment and risk early warning based on random forest algorithm

With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper evaluates and warns security risks of large-scale group activities based on the random forest algorithm, achieving a maximum classification accuracy rate of 0.86 through model optimization and training experiments, demonstrating the algorithm's strong predictive ability in risk assessment.