Aiming at the problem that it is difficult to fully utilize the sequence contextual semantic information and the implicit correlation information among variables in multivariate time-series prediction， a model based on decoupled attention mechanism — Decformer was proposed for multivariate time-series prediction. Firstly， a novel decoupled attention mechanism was proposed to fully utilize the embedded semantic information， thereby improving the accuracy of attention weight allocation. Secondly， a pattern correlation mining method without relying on explicit variable relationships was proposed to mine and utilize implicit pattern correlation information among variables. On three different types of real datasets （TTV， ECL and PeMS-Bay）， including traffic volume of call， electricity consumption and traffic， Decformer achieves the highest prediction accuracy over all prediction time lengths compared with excellent open-source multivariate time-series prediction models such as Long- and Short-term Time-series Network （LSTNet）， Transformer and FEDformer. Compared with LSTNet， Decformer has the Mean Absolute Error （MAE） reduced by 17.73%-27.32%， 10.89%-17.01%， and 13.03%-19.64% on TTV， ECL and PeMS-Bay datasets， respectively， and the Mean Squared Error （MSE） reduced by 23.53%-58.96%， 16.36%-23.56% and 15.91%-26.30% on TTV， ECL and PeMS-Bay datasets， respectively. Experimental results indicate that Decformer can enhance the accuracy of multivariate time series prediction significantly.