Abstract: Under complex working conditions, fully mechanized mining equipment often suffers from insufficient adaptability and poor coordination among equipment units, leading to abnormal operation and potential safety hazards, which restricts the mining efficiency and safety management level of coal mines. Meanwhile, in high-intensity and continuous operation environments, traditional equipment evaluation methods can hardly identify potential risks in a timely manner. To address these problems, an adaptability evaluation system for fully mechanized mining equipment is constructed to conduct quantitative analysis and dynamic evaluation of equipment operating conditions. Focusing on three core pieces of equipment, shearer, hydraulic support and scraper conveyor, four evaluation dimensions are selected: environmental adaptability, functional stability, equipment matching and economic efficiency, with a total of 10 evaluation indicators. The weight of each indicator is determined by the fuzzy analytic hierarchy process (FAHP). Combining variable weight theory, a trapezoidal membership function and a state response mechanism are introduced to establish a dynamic evaluation model. This model realizes real-time perception of changes in key indicators and dynamic adjustment of weights, effectively overcoming the limitations of traditional methods such as fixed weights and strong subjectivity. Taking multiple moments within 30 consecutive days of the 25215 fully mechanized mining face in Hongliulin Coal Mine as samples, a comprehensive analysis is carried out, and 5 typical moments are selected for in-depth dissection, covering various working conditions including maintenance, equipment abnormality and normal mining. The empirical results show that the dynamic evaluation model can accurately reflect the operating status of equipment, and the evaluation results are highly consistent with actual working conditions, which fully verifies the effectiveness of the method. This model can not only identify the weak links in the adaptability of fully mechanized mining equipment, but also realize early warning of potential risks and improve the safety and reliability of working face equipment. It provides scientific and technical support for the management and optimal decision-making of fully mechanized mining equipment, and also offers a replicable method for intelligent equipment evaluation under complex working conditions.