## 1 Introduction  
As the carrier of various cultural and recreational activities, stadiums have many internal facilities, a large flow of people, closed spaces, complex structures, uneven personnel quality, weak awareness of fire prevention and disaster prevention, hidden safety hazards always exist, and a high fire risk. In recent years, the stadiums in China have suffered more than 200 fires, with the deaths of more than 1000 people, which fully reveals the serious problem in the security management of stadiums [1].There are many risk factors for fires in stadiums; the mechanism is complex, and there may be a linear correlation between internal factors, resulting in inaccurate assessment results. In response to this deficiency, existing research also attempts to use some methods to overcome the high-dimensional problem (the curse of dimensionality). [PERSON] et al. [2] proposed a feature selection method based on recursive feature addition and gram techniques and tested it on the ISCX2012 dataset, and the results showed that the performance of the model was significantly improved. [PERSON] et al. [3] used principal component analysis for dimensionality reduction and evaluated their models, which outperformed traditional supervised machine learning (ML) algorithms in terms of accuracy, false positives, and recall. At present, static assessment is the main method for the prediction of fire risk in stadiums, and mathematical statistical methods such as the fuzzy evaluation method [4], Analytic Hierarchy Process (AHP) [5], and structural entropy weight method [6] are mostly used. The selection of the indicator weight is subjective, and some indicators require on-site scoring by experts, which leads to the inability to verify the accuracy of the static evaluation model. [PERSON] et al. [5] ranked and classified various fire factors in urban residential areas based on AHP, and designed a new tool for residential fire risk probability. They developed a fire risk prediction model and a GIS risk hazard map with fire factor classification settings. [PERSON] et al. [6] introduced the structural entropy weight method into the index weight determination process, and established a new large-scale commercial building fire risk assessment system, which can be used as the input data feature to predict the fire safety performance of the building. However, none of them circumvent the subjectivity of indicator quantification and weight assignment. Some scholars have paid more attention to the advantages of ML algorithms in solving the subjectivity of fire risk assessment weights. Yet, the research in the field of dynamic fire risk prediction model construction in stadiums is extremely lacking. In addition, the main working mode of the government and fire safety management departments is \"man-sea warfare to carry out inspections and turns to filter key points\" [7], and relies solely on human experience to subjectively judge whether the fire risk level is high, and the ability to actively detect and give an advance warning is weak. Therefore, it is necessary to establish a high-quality fire decision support system or model to make forward predictions of fires in stadiums, evaluate the possibility or risk level of fires, and discover and deal with high-risk hidden dangers in time. With the continuous maturity of big data analysis technology [8] and ML intelligent algorithms, the application in building fire prediction seems very promising by analyzing massive historical data for forward-looking predictions. Most of the studies focus on predicting property damage [9], casualties [10], accident severity [11; 12; 13], and other ex post evaluation indicators, and they have achieved good results. Building a fire prediction system can be roughly divided into two aspects: community-level building fire prediction and property-level building fire prediction. Community-level building fire prediction: [PERSON] et al. [13] proposed a new framework for real estate fire risk prediction based on statistical machine learning.