Abstract: In view of the problems of high no-load rate, frequent coal blockage in the scraper conveyor, serious coal accumulation in the coal flow, frequent equipment start and stop, and others of the shearer in the 3105 working face of Huoerxinhe Coal Mine, a dynamic model of shearer-scraper conveyor system is constructed based on the centralized parameter method. Key parameters such as cutting depth, machine body inclination angle, coal rock interceptability coefficient and others are identified by adopting principal component analysis and grey correlation degree analysis method. A multi-parameter working condition identification model based on fuzzy neural network and hierarchical collaborative control strategies are designed to achieve adaptive cutting control of the shearer and chain speed regulation of the conveyor based on coal flow prediction. The results show that after the system application, the no-load rate of the shearer is reduced from 36% to 18.6%, the number of starts and stops is reduced from 38 times/shift to 22 times/shift, the stacking height of the coal flow is controlled within 0.58 m (with a reduction of 36.9%), the coal blockage event is reduced from 15 incidents/month to 4 incidents/month, and the comprehensive power consumption per ton of coal is reduced from 5.2 kW·h/t to 4.0 kW·h/t, and the equipment synergy efficiency is improved by 31.5%, saving electrical energy costs annually 2.86 million yuan.