## 1 Introduction  
As the depth of underground coal mining increases, the disasters such as gas explosion, rock collapse and water inrush occur more frequently in the process of mechanized coalface mining, all of which seriously threaten the lives of the human miners [1]. To improve this situation, unmanned fully mechanized coal mining is considered to be an effective method, notably in view of its high recovery ratio and extremely low mortality rate in recent years [2, 3]. The intelligent shearer, one of the main pieces of equipment at a fully mechanized coalface, is key when using this method. However, it has always been difficult to regulate the shearer drum height to fully realize the intelligence ability of the shearer, since the natural occurrence boundary of coal seams is very irregular due to frequent sinking of roof rocks and rising of floor rocks. To solve the problem of intelligent height adjustment of coal mining machinery, the early methods adopted by researchers were simple sensing adjustment methods based on coal-rock interface recognition, such as \(\gamma\) ray detection [4], radar detection [5], image recognition [6], infrared temperature detection [7], etc. However, due to the complex geological conditions of coal mines, the coal seam state and coal rock properties are so variable that the above methods are not universally applicable, and these techniques have not been applied in engineering due to the harsh working face environment and the difficulty of real-time identification [8]. To overcome these issues, the researchers proposed the memory cutting technology of indirect perception of the coal-rock interface. By contrast with the coal-rock interface identification method, memory cutting technology is the next cutting path predicted by historical cutting parameters, and does not use the coal-rock interface as the basis for the height adjustment of the shearer drum, which is more convenient for actual operation [9]. Although the traditional memory cutting technology can reduce the number of underground workers to a certain extent and improve the degree of automation of the shearer, due to the complex environment of the comprehensive mining face and the drastic changes in the coal-rock interface, the memory path fails; thus, the traditional memory cutting technology relies on human intervention, and its cutting accuracy [10] is low. To solve this problem, [PERSON] et al. proposed a memory cutting method of adjacent coal seams based on hidden Markov model and using the correlation of adjacent coal seam data [11]. [PERSON] et al. built a depth LSTM model by inputting the truncation trajectory data of the previous moment, and introduced the scale factor to compensate the response attenuation, which optimized the prediction accuracy of the model for the drum cutting track [12]. However, because it is difficult to describe the complex coal seam state only depending on the coal seam cutting track information, [PERSON] and [PERSON] did not achieve the expected results. To further improve the adjustment and precision, [PERSON] et al. analyzed the coal and rock state through multiple data, and optimized the cutting path of the shearer drum based on a genetic algorithm [13]. [PERSON] et al. used the virtual memory cutting method based on a virtual cutting process to predict the AFC shape, and used SVM and ELM network to conduct multi-input information analysis and roll height prediction [14]. [PERSON] et al. established a fuzzy cut-off rule set by analyzing multiple cut-off data attribute parameters, and optimized the cut-off path of the shearer drum by using fuzzy cut-off rule decision [15]. [PERSON], [PERSON], [PERSON] and others adopted multi-sensor data fusion methods to reveal the complex mapping relationship between the input parameters and the cut-off track of the shearer drum, and improved the cut-off accuracy of the shearer drum.