## 1 Introduction  
There are a large number of medium-thick coal seams in more than half of the main minable coal seam in the Yushen mining area in northern Shaanxi, and the geological conditions are relatively simple, with great excavation conditions. One of the most effective ways to reduce the drivage per ten thousand tons of coal, improve the productivity and efficiency of the mining face, and reduce the loss of coal is to use ultra-long mining faces [1]. However, due to the increase in the length of the mining face, the behavior of mine pressure and its spatial distribution on a fully-mechanized mining face are different from those of typical mining faces. This results in great difficulties in roof management and the intelligent control of ultra-long mining faces [2; 3; 4]. Mine pressure behavior monitoring, pressure prediction and warning, and intelligent control are the keys to realizing the normal operation of the intelligent control system in ultra-long mining faces. By analyzing the working resistance of the supports of the ultra-long mining face and the pressure characteristics, deep learning algorithms have been used to achieve an accurate prediction of the pressure on the mining face, which is of great significance to realizing the safe, intelligent, and efficient mining of medium-thick coal seams. In recent years, intelligent mining in China's coal mines has developed rapidly, and a number of intelligent mining faces (e.g., Huangling No. 2, Hongliulin, and Balasu Coal Mine) have been built. This has led to the intelligent sensing of strata information and the automatic control of key production systems and equipment in the mining face. Hydraulic supports are one of the main instruments used in the mining face. The posture, bearing capacity, and load distribution of supports represent the interaction between the support and the surrounding rock of the working face. State monitoring based on massive monitoring data for the support resistance of the mining face, including state sensing, analysis-discrimination, and autonomous adjustment, is the key to the intelligent control of the production system of the mining face [5]. Based on field monitoring data for support resistance in mining faces, a number of studies have investigated the pressure prediction in fully mechanized mining faces using methods such as the expert system, neural network, and big data analysis methods. For instance, back-propagation (BP) neural network models were constructed in several studies [6; 7; 8], and the performances of these models were verified using field monitoring data. At the same time, deep learning algorithms based on big data analysis have also been widely used [9; 10; 11], and great progress has been made in the establishment of a pressure-prediction model of the mining faces. [PERSON] et al. [12] extracted the characteristic parameters during the working cycle of the support and constructed an intelligent sensing system for the supports and roof of a fully- mechanized mining face, achieving a quality evaluation of the support and an intelligent prediction of roof weighting. [PERSON] et al. [13] used a relational database to store working resistance data and extracted the time-series characteristics of the hydraulic supports during the advancement of the mining face. Then, they employed a long-short-term memory network (LSTM) to establish a pressure-prediction model. With the help of transfer learning, they proved that the LSTM model had a good generalization ability in roof-weighting prediction. Based on the time-series distribution of the working resistance of a support and the characteristics of complex working conditions, [PERSON] et al. [14] developed a classification modeling method for support loading based on a clustering algorithm. Additionally, [PERSON] et al. [15] established a Prophet + LSTM model for pressure prediction on a mining face by integrating the working resistance data for adjacent supports using additional regression variables models.