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

Abstract

• With the continuous development of artificial intelligence, machine learning, as an indispensable means to realize artificial intelligence, is constantly improving, and deep learning is one of the contents. This article aims to evaluate and warn the security risks of large-scale group activities based on the random forest algorithm. • In this paper, the computational random forest algorithm is used to calculate the importance of variables and the security risk index weight, and combined with the model parameters of the random forest algorithm, optimization experiments and random forest model training experiments are carried out respectively. At the same time, an international youth environmental protection festival is taken as an example to analyze, which has verified the feasibility and effectiveness of this article. • This article mainly evaluates the risks in large-scale group activities, but it can be further improved in future applications. On the basis of it, if the activities want to achieve better results, they must also satisfy the people who participate in the activities. Thereby, it can better help resolve some unnecessary risks and ensure the safety of people in their activities. 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.