## 3 Data Preparation and Pre-Processing
### Selection of Data  
This paper is based on the industrial mining equipment configuration standards [39] as the fundamental theory, combined with relevant three machines selection knowledge from the mining engineering design manual [40]. It selects 26 factors related to equipment selection as the initial input features for the three machines parameter prediction model of the fully mechanized mining face, with the input feature variables shown in Table 1. The category of the roof and floor is used to replace the vague lithology expression, which improves the model's prediction accuracy. Moreover, since the category of the roof and floor has a strong linear or categorical relationship with factors such as the cutting coefficient, rock hardness coefficient, mining machine startup rate, and recovery rate of the fully mechanized mining face, using the roof and floor category as an input feature effectively reduces the input feature dimensions and improves the training speed. The basic roof type is classified from weak to strong based on a comprehensive reflection of the basic roof thickness, tensile or compressive strength, and the degree of fissure weakening, divided into four categories: Category I (non-obvious), Category II (obvious), Category III (strong), and Category IV (very strong). Among these, Category IV is further divided into Category IVa (very strong a type) and Category IVb (very strong b type). The immediate roof type is classified from unstable to stable based on the hardness and brittleness characteristics of the rocks that make up the roof, divided into four categories: Category I (unstable), Category II (moderately stable), Category III (stable), and Category IV (very stable). Among these, Category I is further divided into Category Ia (unstable a type) and Category Ib (unstable b type). The floor type is classified from low to high based on the allowable load-bearing strength, divided into five categories: Category I (extremely soft), Category II (loose), Category III (relatively soft), Category IV (medium hard), and Category V (hard). Category III is further divided into Category IIIa (relatively soft a type) and Category IIIb (relatively soft b type) [41, 42]. The 12 important parameters for selecting the three machines are chosen as output features [43], and the output feature variables are shown in Table 2. Among them, fully mechanized mining face cycle yield refers to the total amount of coal mined in a complete production cycle of the three machines, reflecting the adaptability of the mining equipment to the mining face. The number of cuts refers to the number of times the coal cutter cuts into the coal seam during a working cycle. Typically, the number of cuts should be controlled to within 20 to maintain normal operation. Using fully mechanized mining face cycle yield and number of cuts as output features can effectively assess the rationality of the prediction results [44]. The training sample data are sourced from China Coal Xi'an Design Engineering Company, Limited, located in Xi'an, China representing equipment selection instances under different working face conditions from multiple mining areas. The geological parameter range is comprehensive, and there is significant variation in the design parameters of the fully mechanized mining equipment. To ensure the general applicability of the method, during the data collection and organization phase, the sample set was established with conditions including a daily working time of 8 hours per shift, two shifts of work with one shift for maintenance, coal seam burial depth less than 1400 m, coal seam thickness less than 20 m, and a coal seam dip angle of less than 15 degrees for gently inclined fullymechanized mining faces. A total of 1376 data sets were collected. A portion of the sample data is shown in Table 3.