## 1 Introduction  
To meet the urgent needs of the country and coal mines for intelligent equipment, especially during the mining process, the importance of rapid support by anchor rod drilling machines for tunnel excavation cannot be ignored [1]. However, with the increasing complexity of the underground strata environment, the acquisition of middling coal rock mass strength information during the drilling process of the drilling rig is still mainly dependent on static rock mechanics experiments and geological exploration, which has obvious lag and cannot achieve real-time acquisition. During the drilling process, due to the complexity of rock fragmentation and the various responses of drilling rigs under different formations and working conditions, real-time adjustment of control parameters becomes particularly crucial. The current lag has caused the control parameters of the drilling rig to be unable to be adjusted based on real-time coal and rock strength during the drilling process, leading to a series of problems, including severe drill bit wear, low construction efficiency, and safety construction difficulties. Therefore, to achieve the safety, intelligence, low-cost, and high-efficiency support goals of underground drilling operations, there is an urgent need for an intelligent prediction technology for coal and rock mass strength based on drilling signals. This technology will be able to quickly and in real time identify lithology, provide accurate coal and rock strength information for support operations, guide underground drilling operations, effectively respond to the working conditions of drilling rigs in complex geological environments, and ultimately achieve safe, intelligent, low-cost, and high-efficiency support goals. When drilling anchor rods into different formations, the drilling rig will produce various responses, and the physical and mechanical properties of different rocks make the vibration, torque, and speed of the drilling rig different [2; 3; 4]. Deep learning network models have been widely applied in the intelligent recognition of coal and rock masses [5]. Ding computer technology has achieved good results in predicting the uniaxial compressive strength of rocks in recent years, using methods such as regression analysis [6], artificial neural networks [7; 8], fuzzy inference systems [9], etc. [PERSON] realized the noise reduction, amplitude processing, and fault feature analysis of vibration signals using a nonconvex variable function and algorithm [10]. [PERSON] [11] developed a parameter acquisition system for hydraulic anchor rod drilling machines in tunnels, identifying rock mechanical properties by drilling parameters. [PERSON] [12] conducted drilling experiments on a drilling test bench and established a fuzzy recognition method with a clustering model to classify typical coal-bearing strata, and the final accuracy rate was 92%. [PERSON] [13] obtained the response relationship between drilling parameters and strata and used the support vector machine (SVM) classification algorithm to establish a coal rock interface recognition model based on drilling parameters, achieving automatic recognition of coal rock interfaces. [PERSON] [14] integrated multiple regression models using the stacking ensemble algorithm and established a stacking ensemble model for predicting the uniaxial compressive strength of rocks based on four basic parameters of rock characteristics. The results showed that the model was feasible and reliable. [PERSON] [15] established a rock compressive strength prediction model based on three mainstream ensemble algorithms based on existing triaxial test data of different rocks. Bayesian optimization was used to complete hyperparameter optimization, and the generalization ability of the model was evaluated. The importance of rock characteristics was identified. [PERSON] [16] collected vibration signals in the laboratory and established a dataset through processing, establishing an improved VGG11 convolutional neural network for the lithology intelligent prediction model.