As mining depth gradually increases, the complex and changeable behavior state of mine production systems leads to the increasingly prominent problem of “planning is difficult to control”. The safety production situation and the difficulty of emergency management are gradually upgraded. However, the theoretical study on the behavioral trend of complex mine production systems is still an immature field. This paper proposes the scientific problem of “theory and method of time-varying computational experiments for fully mechanized mining processes in artificial system environments”. Taking the typical fully mechanized mining process in the Yushen mining area in northern Shaanxi, China, as the research object, through computer modeling, simulation and use of multiagent system theory, APSM (Agent Publish-Subscribe Model) coordination technology, multiagent cross-emergence and multilayer learning networks, the artificial fully mechanized mining system modeling, sequential mining process deduction and state transfer theory are systematically studied. First, an artificial system model equivalent to the function of the actual fully mechanized mining system is constructed. Then, under the artificial system environment, the time-varying computational experiments of the fully mechanized mining process are realized through the autonomous deduction of the fully mechanized mining agent based on a multilayer neural network and the emergence of multiagent interactions based on subscription perception; this approach aims to solve the problem of determining the overall behavior trend of the mine under the condition of “long time and large space” and to provide intellectual support and scientific basis for the “first experiment and then produce” technological model of intelligent mining.