Abstract: In order to systematically analyze the key causes of miners’ unsafe behavior, based on the complexity of behavioral causes, this paper studies the distribution characteristics of unsafe behavior from the dimensions of time, place, behavior classification, department, position and degree of violation. Adopting the Apriori algorithm, strong association rules from many association rules of unsafe behavior of miners are found. The results show that during the early shift, the number of unsafe behavior is the largest among the three ones and the highest rate of unsafe behavior is that in the yard; among the 26 types of unsafe behavior, the incorrect wearing of safety operating equipment is the most likely unsafe behavior; the fully mechanized excavation teams account for the largest number of unsafe behavior among the 9 responsible departments; the common miners produce the most unsafe behavior; and the majority of unsafe behavior violations are at the general level. In addition, focus on preventing and controlling the unsafe behavior of common miners in the early shift with the degree of general violation should be put, the safety inspection of the yard or up/down trough needs to be strengthened, and the unsafe behavior of the incorrect wearing of safety operating equipment is supposed to be strictly controlled.