Abstract: In order to analyze the influence of light environment on the brain fatigue of support workers, identify and alleviate the brain fatigue of support workers in fully mechanized excavation face, and reduce coal mine safety accidents, a brain fatigue induced experiment was designed, and the subjective and objective data were collected and analyzed. The evaluation indexes of brain fatigue were extracted by single factor analysis of variance and paired sample T-test. Support vector machine, K-nearest neighbor algorithm and random forest algorithm were used to construct the brain fatigue recognition model of the support worker in fully mechanized excavation face, and the confusion matrix was established to comprehensively compare the recognition effect of each model, and the optimal recognition model was selected. The results showed that the accuracy rate, response time, NASA-TLX value and δ,θ, α and β rhythm energy values of O₁, O₂, O₆, O_Z, P_O4, P_O8, P_OZ and T₈ electrode channels could be used as the evaluation indexes of brain fatigue of the support workers in fully mechanized excavation face. The recognition accuracy of the three types of recognition models is high, the accuracy of support vector machine is 94.44%, the accuracy of K-nearest neighbor is 96.30%, and the accuracy of random forest is 90.74%. Therefore, the recognition model of brain fatigue of support workers in fully mechanized excavation face based on K-nearest neighbor algorithm has the best recognition effect.