Aiming at the problems of insufficient control accuracy and poor adaptability to complex working conditions of DTL80/50/2×132 belt conveyor tensioning device in Shanxi Xinjing Mine, an automatic tensioning intelligent control scheme combining fuzzy PID and intelligent algorithm is designed. By constructing the dynamic tension expectation model, the particle swarm optimization algorithm is used to set the key parameters of the controller offline, and the radial basis function neural network is introduced for feedforward compensation. The measured results show that the scheme can effectively suppress the tension fluctuation, reduce the steady-state deviation and improve the response speed of the system, which has engineering application value.