Abstract: In view of the poor accuracy of performance analysis of shearer drum height adjustment hydraulic system by using idealized signal to simulate drum load， it is difficult to realize self⁃adaptive control of shearer drum height ad⁃ justment， poor response speed and tracking performance based on traditional optimization control algorithm.A self⁃ adaptive height adjustment control strategy of shearer drum based on depth deterministic gradient algorithm DDPG is proposed，and using virtual prototype technology， deep convolutional neural network and deep reinforcement learning and other machine learning algorithms to build an integrated hydraulic control system for shearer self⁃adaptive height adjustment.The rigid flexible coupling dynamic simulation model of shearer height adjustment system is established by using Pro / E and RecurDyn. According to the actual occurrence conditions of a coal face， the discrete coal wall model is established by using EDEM， the mechanical system model of EDEM⁃RecurDyn bi⁃directional coupling height ad⁃ justment mechanism is constructed based on DEM⁃MFBD interface， and the hydraulic system model of height adjust⁃ ment mechanism is established based on AMESim. A self⁃adaptive height adjustment control system model of shearer is built by using Simulink， which integrates six modules: signal processing module(Signal processing)， time spec⁃ trum diagram generation module(Continuous wavelet transform System)， data sample expansion module(Fancy PCA System)， cutting state identification module(Alexnet Transfer Learning System)， height adjustment control deci⁃ sion module(Height Control decision) and DDPG height adjustment model module(DDPG Height Adjustment Model). Based on the interface technology， the integrated hydraulic control system model of shearer self⁃ adaptive height regulator based on EDEM⁃RecurDyn⁃AMESim⁃Simulink multi domain collaborative simulation is built. The system model is used to simulate and analyze its height adjustment performance. The research results show that the accurate identification of coal and rock cutting state can be realized based on continuous wavelet transform， fancy PCA and Alex net network transfer learning，and the recognition accuracy rate can reach 95.58%.The simulation process of the built system can more truly simulate the coal and rock cutting and crushing process of the shearer. The sys⁃ tem can perceive the change of the cutting working condition after only about 0.6 s， quickly identify the coal and rock cutting state， accurately adjust the drum to the target height， and has fast response speed， It can adaptively ad⁃ just the piston speed according to the change of working conditions.Compared with fuzzy PID control， the maximum steady⁃state error of piston retraction displacement of self⁃adaptive height adjustment system of shearer based on DDPG control is only 0.002 1 mm， only 0.66% of the former. Compared with the control performance in the stable stage before and after height adjustment， the fluctuation of piston movement speed and hydraulic cylinder chamber flow of fuzzy PID control system increases significantly， while the difference of DDPG control system is small， It shows that the latter has stronger adaptability and is more suitable for the self⁃adaptive control of shearer height adjust⁃ ment hydraulic system under the condition of complex coal seam.The test verifies the feasibility and correctness of the shearer’s adaptive height adjustment control strategy and simulation results， which effectively improves the shearer’s adaptability to complex coal seams and promotes the development of coal mine intelligence.