Taking 1420 mm UCM six-high cold rolling mill as the research object, a non square flatness control system with five input and four output is decoupled into a square subsystem with two input and two output which controls the primary and cubic flatness and a non square subsystem with three input and two output which controls the quadratic and quartic flatness by using the relative gain theory. By decomposing the unstable poles of the generalized inverse matrix of the non square system, the method of the generalized inverse matrix decoupling control the quadratic and quartic flatness is proposed, which solves the unstable problem of decoupling of non-square system. According to the characteristics of intermediate roll shifting, the variable model of roll shifting influence coefficient and the control strategy of minimum roll shifting adjustment and threshold are proposed. The dynamic characteristics of the system are improved and the adjustment of intermediate roll shifting is reduced. In order to overcome the shortcomings of low accuracy and poor generalization ability of shallow neural network, a mechanism-intelligent influence matrix model based on big data and deep neural network is proposed. Simulation calculation and industrial application show that the control system runs stably, the adjustment speed is fast, the control precision is high, the change of intermediate roll shifting is small, and it is suitable for online control.