## 1 Introduction  
Coal spontaneous combustion in goat areas is characterized by secondary effects, concealment, and uncontrollability, which can easily trigger secondary disasters, such as coal dust and gas explosions [1, 2, 3]. Several catastrophic accidents have occurred due to this phenomenon, including the gas explosion triggered by coal spontaneous combustion at the Jilin Tonghua Babao Coal Industry in 2013, a similar incident at the Yanshitai CoalMine in 2014, and the gas explosion caused by the spontaneous combustion of residual coal at Tangyang Coal Mine in 2018. These incidents highlight the critical importance of accurate prediction and early warnings for coal spontaneous combustion for ensuring safe coal mine production [4; 5; 6]. Coal spontaneous combustion involves complex physicochemical processes that result in heat accumulation within the coal mass, eventually leading to spontaneous ignition as temperatures rise [7; 8]. During this process, various indicator gases, including CO, CO\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), C\({}_{2}\)H\({}_{6}\), and C\({}_{2}\)H\({}_{2}\), are released, with their concentrations exhibiting distinct trends with increasing coal temperature [9]. The traditional gas analysis method has been widely adopted due to its non-invasive nature and real-time monitoring capabilities [10]. While the Coal Mine Safety Regulations categorize coal spontaneous combustion into three stages (incubation, self-heating, and spontaneous combustion periods), this classification is overly simplistic and lacks quantitative analysis [11; 12]. Although numerous researchers have conducted experimental studies to establish quantitative relationships between indicator gases and warning levels [13], the highly nonlinear relationship between these parameters makes it challenging to obtain accurate predictions using traditional mechanism-based methods [14; 15]. Recent studies have also revealed that environmental factors, such as the temperature, humidity, and oxygen concentration, significantly influence the spontaneous combustion process, further complicating the prediction problem [16; 17]. With the rapid advancement of computer science and artificial intelligence, many scholars have applied machine learning methods, such as BP neural networks [18; 19], support vector machines [20; 21], and random forests [22; 23], to predict coal spontaneous combustion. These data-driven approaches have shown promising results in laboratory conditions, demonstrating superior performance compared to traditional statistical methods [24]. However, these supervised learning approaches require labeled training samples, which are often difficult to obtain in practical situations [25]. Furthermore, existing classification standards for warning levels vary significantly due to differences in the project backgrounds, testing methods, and coal sample selection, with the number of warning levels ranging from 3 to 7 across different studies. This inconsistency in classification standards, coupled with varying severity interpretations of the same warning level, hampers the effective implementation of prevention and control measures. To address these limitations, this study analyzes 336 sets of data collected through oil bath experiments with programmed temperatures across 21 different mining areas and proposes a novel unsupervised learning approach combining t-SNE and k-means clustering for warning level classification. The method integrates machine learning techniques with traditional coal mine safety practices, establishing a comprehensive warning level system based on multiple indicator gases. Our approach differs from previous studies by eliminating the need for pre-labeled training data and providing a more objective classification framework. The effectiveness of the proposed approach is validated through field application in actual mining conditions, providing a practical solution for spontaneous combustion prediction in goat areas.