Risk Evaluation Model and Intelligent Assessment of Large Scale Sports Events

摘要

There are multiple deviations between the expected and actual risk of large-scale events, and it is not easy to be evaluated by quantitative model. Therefore, this paper uses the basic principles of AHP and BP neural network to establish the risk assessment model of the event. In the process, particle swarm optimization algorithm is used to optimize the weights of BP neural network and to improve the defects of BP neural network, such as slow convergence speed and easy to fall into local extremum. Then, through the establishment of membership matrix, the data of actual activities are acquired, and the neural network model is trained and tested by using Matlab mathematical software. The test results show that our scheme can effectively analyze the risk factors existing in each link of the event, and provide a scientific basis for making effective preventive measures and management decisions.