## 1 Introduction
The research results show that the optimal model of the artificial neural network for evaluating the frequency of catastrophic fires can detect and predict the occurrence of fires in a timely and accurate manner. [PERSON] et al. [12] proposed a cross-region transfer learning approach to identify fire hazard frameworks in communities such as parking lots, public spaces, and shopping malls. Dividing the community fire danger into nine grades, its recognition performance has been improved by 12%, 15%, 16%, 15%, and 15% in overall accuracy, precision, recall, F1 score, and AUC, respectively. Property-level building fire prediction: This level refers to risk prediction studies that predict fire risk in terms of property damage and casualties. In Pittsburgh, Pennsylvania, [PERSON] [9] proposed a framework for building fire risk predictions. Models in this framework predict a construction fire score of 1-10 (lowest risk to highest risk) for building properties. In the experimental results, the recall value of the XGBoost model is 0.55, and the AUC value is 0.77. [PERSON], [PERSON] [14] constructed a framework for predicting fire risk in buildings in London. It uses Fire Brigade incident data, aerial imagery, and a digital surface model (DSN). The final model presented achieved an ROC AUC of 0.8195 on the test set. Firebird [10] is a model for predicting building fire risk in Atlanta. It uses fire event data (time, location, and cause of fire), commercial property structure data, and property fire risk inspection data and predicts a fire risk score between 0 and 1 for building properties. The evaluation result is that the random forest (RF) model performs the best with an AUC value of 0.8246. However, ML algorithms are not one-size-fits-all, and the results found in other studies cannot be used to determine that this classifier will perform best with minimal error on the Chinese stadium dynamic fire risk prediction dataset. Due to the variety of ML algorithms, the algorithm that is more suitable for dynamic fire risk prediction in stadiums has not been scientifically verified. It is necessary to carry out multi-dimensional experimental research on various classification algorithms and model testing methods. In this paper, to further assist stadiums in fire supervision management and resource planning, a gradient boosting-recursive feature elimination (GB-RFE) method is designed to extract fire risk features and reduce the redundancy of features. Then, a multicollinearity test was performed on the optimized feature subset using Pearson correlation analysis to eliminate strongly correlated risk factors. The obtained optimal feature subset is used as the input data set of the ML algorithm for classification training. To avoid the influence and analysis of redundant features on the model prediction results, the model performance and operation efficiency can be improved. A fire risk prediction method based on k-fold cross-validation and gradient boosting decision tree fusion is proposed. Its main purpose is to allow the fire management department of the unit to gather fire resources to rectify or eliminate major fire hazards for the first time, and nip more fire hazards in the bud. The practical significance of the experimental results is that the use of the ML model can speed up some information analyses, and make them more objective and effective in predicting performance than based on human subjective analysis, providing a scientific basis for the prevention and management of stadium fire accidents. Summarily, the main contributions of this paper are:  
* We propose a risk prediction model of a gradient boosting decision tree combined with the K-fold cross-validation strategy, which can effectively predict the fire risk level of stadiums based on dozens of factors. We show that with basic information about stadiums (fire acceptance status, fire host failure rate, stadium size, etc. ), we can predict in advance the likelihood of a stadium fire in the future.