## 1 Introduction
In recent years, the numerical calculation models (e.g., FEM and FVM) for a single line of surrounding rock-airflow heat transfer have been included in the scholars' discussion scope [21; 22; 23]; nevertheless, since the relationships between the air temperature and numerous influencing factors (e.g., wall roughness, thermal physical parameters of rock, and moisture content of airflow) are highly nonlinear, scholars find it extremely hard to figure out parameters in these models. In addition, the models are rarely applied to whole ventilation networks on engineering sites as a result of missing parameters, deficient validation, and insufficient an description of the heat and mass transfer mechanisms. In fact, the heat released by thermal water upwelling from rock fractures or by high-temperature surrounding rocks coming into contact with the thermal water is non-negligible in the calculation of heat sources in a mine [24; 25; 26]. The mass transfer in a roadway with humid air is less focused on. [PERSON] [27] and [PERSON] [28] introduced the \"wall wetness degree\" to correct \(\rm K_{\tau}\), and [PERSON] [29] constructed an airflow heat exchange coefficient correction function for a roadway with water trickling, using an airflow rate weighted distance to describe the change law of relative humidity of the airflow in an actual mine network area. As described above, research efforts on the prediction of underground airflow humidity and temperature are insufficient, for which the reasons are as follows: (1) the factors influencing temperature or humidity are numerous; (2) it is hard to master the coupling law between heat exchange and moisture exchange; and (3) the usability is insufficient, and the prediction accuracy may be high for a single or short-distance line but the models cannot be popularized to the whole network or long-distance lines. Numerical simulation is an effective method for scholars to study the change of temperature and humidity in a roadway. Based on numerical simulation, [PERSON] et al. [30] and [PERSON] et al. [31] explored the flow and heat transfer law of thermal fluid during geothermal mining in rock strata. Their exploration results revealed that the temperature of the surrounding rock of a roadway decreases gradually, and the temperature rise of the air flow in a roadway is reduced during the process of water injection in rock strata. [PERSON] [32] investigated the effect of the seepage of cryogenic fluid on the surrounding rock temperature, and found that the fissure seepage can control the distribution of surrounding rock temperature in a high-temperature roadway. Nevertheless, the modeling process in numerical simulation always deviates from the actual situation on the spot, leading to unsatisfactory results. Moreover, the numerical simulation cannot be updated in real time, thus failing to predict the change of tunnel climate effectively. Machine learning (ML) refers to learning algorithms adopted for searching the optimum prediction model with the aid of big data [33; 34; 35]. In recent years, ML has been widely employed in mining engineering applications, such as gas emission [36], mine water inflow [37], shield pressure monitoring [38], and air quality and pollutant monitoring [39], to adapt to such characteristics of various problems under the combination of multiple factors such as a large number of data, and strong non-linearity [40; 41; 42]. In terms of predicting the underground microclimate, [PERSON] [43] established an actual mathematical model based on the thermal conductivity inside tunnels, and compiled a corresponding program for prediction, which has been applied to practical projects. However, the influence of cryogenic fluid on temperature was omitted in this mathematical model. Currently, problems remain in utilizing ML to predict the underground microclimate.