Application of Soft Computing in the Prediction of Audience Behavior in Sports Events Using Fuzzy Set Theory

The behavior of sports event spectators is influenced by various factors, and the relationship between each influencing factor is vague. The traditional analysis method for audience behavior in sports events cannot effectively handle the non-linear relationship of audience behavior and is challenging to address the ambiguity and uncertainty. Fuzzy set theory was combined with neural networks better to handle the nonlinear relationships and complexity of audience behavior. A large amount of audience behavior data was collected and preprocessed to ensure the quality of the data. Through fuzzy set theory, the data was fuzzified, and a series of rules was established to create a fuzzy rule library. Data analysis was conducted using fuzzy reasoning, and ambiguity resolution was successfully achieved. By combining fuzzy set theory with backpropagation neural networks, a fuzzy neural network (FNN) was employed to predict audience satisfaction with the event. The experimental results showed that the average accuracy of FNN in predicting audience satisfaction was 96.4%. Based on fuzzy set theory and combined with backpropagation neural networks, a comprehensive analysis of audience behavior in sports events can be conducted to accurately predict audience satisfaction.