Abstract: The extraction method of different data structure features may affect the quality monitoring performance in industrial process. Hence, this paper proposes an integrated quality monitoring (E-MDSQM) method, which integrates the global and local structure features of the process data set method. Firstly, three basic models of partial least square (PLS), neighborhood preserving regression (NPR), and local principal component regression (LGPCR) are constructed to describe the global structure of the data, the local topology, and the local global hybrid structure information, respectively. And then, by introducing a new monitoring index, the genetic optimization algorithm is used to obtain the optimal weight, and the integration statistics are integrated, and the control limits are determined. Finally, the simulation via the Tennessee-Eastman process (TE) process is made to evaluate the monitoring effect of the integrated model and the comparisons with the three basic algorithms of PLS, NPR and LGPCR are also undergone. It is shown from these the experimental results that the integrated model can achieve better comprehensive effect.