Abstract: The anti-punching drilling robot is essential for pressure relief operations in high-stress mines. Its accuracy in identifying coal and rock properties directly impacts drilling efficiency and pressure relief effectiveness. This paper addresses the current reliance on manual experience for coal-rock drilling state recognition, which suffers from low accuracy, long response times, and inability to meet unmanned drilling and pressure relief requirements. Based on a one-dimensional convolutional neural network (1DCNN) and long short-term memory (LSTM) combined with simulation experiments, a coal-rock properties recognition method for the drilling process is proposed. The model's recognition accuracy is enhanced by incorporating a convolutional block attention mechanism (CBAM), and the improved dung beetle optimization (IDBO) algorithm is employed to further optimize the model's hyperparameters, determining the optimal network parameter combination. Coal and rock drilling simulation test bench is constructed, featuring six types of representative coal and rock test blocks. Four categories of sensor signals, including rotation speed, rotation torque, feed speed, and feed pressure, are collected to conduct corresponding comparative testing and analysis. Results demonstrate that the proposed method achieves high coal-rock drilling recognition accuracy of 97.00%, significantly outperforming 1DCNN, 1DCNN-LSTM, logistic regression, support vector machines (SVM), decision trees, random forests, K-means clustering, and Transformer approaches.