## 1 Introduction
In this study, the rough set decision rule extraction system replaces the rule determination method of the fuzzy radial basis function neural network, determines the network model structure according to the decision rules, reduces the complexity of the model structure, and introduces the fuzzy rough membership value as the connection weight value between the hidden layer and the fuzzy rough membership layer to enhance the adaptability of the model to uncertain information and improve the prediction accuracy. Finally, an adaptive immune genetic algorithm is designed to optimize the network parameters and realize the intelligent adjustment of the shearer drum height. In summary, the research proposes the AR-AIGA-FRRBFNN shear drum intelligent height adjustment model. The main contributions of the article are as follows:  
First, an intelligent height adjustment method of shearer drum height based on AR-AIGA-FRRBFNN is proposed, which: accurately describes the complex relationship between coal seam state and shearer drum; predicts the change trajectory of shearer drum height with high accuracy. Second, to remove the redundant feature parameters in the characteristic parameters of shearer operation, we: design a rough set-based significance algorithm for attribute simplification; selectively remove the partial simplification of redundant attributes; construct the optimal attribute simplification set; and improve the prediction accuracy of drum height. Third, in order to adapt to the irregular changes of coal-rock interface and accurately describe the complex coupling relationship between coal-rock state and shearer drum: a fuzzy rough radial basis network model based on decision rule inference is proposed; a deep neural network model is constructed through the decision rule inference system; and the fuzzy rough affiliation \(\eta\) is used as the weight between the hidden layer and the fuzzy rough affiliation layer, so that the network has the ability to measure uncertainty information and achieve high accuracy in predicting the height of shearer drum. Finally, to further improve the prediction accuracy of FRRBFNN on shearer drum height, we: designed an adaptive immune genetic algorithm to optimize the parameters of the FRRBF network; determined the AIGA fitness function according to the direction of population evolution; and calculated the optimal crossover rate and mutation rate according to the adaptive algorithm, significantly improving the algorithm performance and effectively optimizing FRRBFNN parameters. The rest of this study is as follows: the first section introduces the overall framework of this study; the second section introduces the theory of rough set, the definition of the fuzzy rough membership function and the theoretical model of Radial Basis Function neural network; the third section introduces the AR-AIGA-FRBFNN shearer drum height prediction model proposed in this study; the fourth section is the experimental section: through the collected Yujialiang historical cutting data, the initial decision table is established, the attribute reduction based on the significance algorithm is carried out for the initial decision table, the attribute reduction set and decision rule set are constructed, the AR-AIGA-FRBFNN shearer drum height prediction model is constructed through the decision rule set, and the attribute reduction set is used for model training--describing the whole process of shearer drum height prediction and height adjustment control--and the model algorithm is compared and verified; the fifth section is the conclusion.