Abstract: The recognition of cutting state of coal-rock is the key technology to realize “unmanned” mining in coal face. In order to realize a real-time perception and accurate judgment of coal-rock cutting state information, combined with virtual prototype technology, a coal-rock cutting state recognition scheme based on CPS (Cyber Physical Systems) was proposed. It integrated heterogeneous data such as coal-rock cutting state information acquisition, processing, recognition and so on in multiple fields. The discrete element models of coal-rock with different occurrence conditions were developed. The rigid flexible coupling virtual prototype model of shearer cutting part was established. Using the DEM-MFBD (Discrete Element Method-Multi Flexible Body Dynamics) two-way coupling technology to ensure the real-time transmission of motion information and coal and rock state characteristic signal data, the vibration signal of shearer cutting coal and rock was obtained and converted into some two-dimensional time-frequency images by STFT (Short-Time Fourier Transform) algorithm. Combined with the characteristics of time-frequency information, the module of coal and rock cutting state information recognition was built. A method of coal-rock cutting state recognition based on the DCGAN-RFCNN (Deep Convolutional Generative Adversarial Networks-Random Forest Convolutional Neural Networks) network model was constructed. By using improved DCGAN network to expand the time-frequency image, and the gradient penalty term was added to enhance the ability of composite samples to maintain the characteristics of the original samples. The coal-rock time-frequency image data set with 5000 composite samples in each simulation condition was generated. The original simulation data set and composite sample data set were mixed as the training set and test set of coal-rock cutting state recognition network. The improved RFCNN algorithm was used to train the model and get the recognition results. The data sets of different numbers of synthetic samples and the network models of different recognition methods were selected for contrast analysis. The results show when no composite sample is added to the RFCNN identification network, the average recognition rate reaches 89.74%. With the increase of the number of composite samples, the recognition rate of coal-rock cutting state is improved. When the number of composite samples is 5000, the recognition effect is the best, and the average recognition rate reaches 98.09%, which verifies the superiority of using improved DCGAN network to enrich data sets. Compared with the CNN, PSO-BP, BP network models, the RFCNN network model has fast convergence speed, strong generalization ability and high recognition rate, and has significant effect in coal-rock cutting state recognition. It can determine complex occurrence conditions such as soft rock hard coal and more gangue layers. The DCGAN-RFCNN network was experimentally verified through the constructed coal and rock time-frequency spectrum image data set, and the recognition rate of the state of coal rock cutting calculated by using the confusion matrix was 98.41%. The results are close to the simulation results, which verify the feasibility of the method. With the Simulink simulation platform, the coal and rock cutting state recognition system based on CPS perception analysis has been constructed to realize real-time data sharing, online perception and control, so that the shearer has the ability of intelligent cutting.