There is strong dependence among the variables of multivariate time series，which makes the data trend unobvious and the prediction difficult. Traditionally，recurrent neural network with gating mechanisms and its variants are used for prediction. But the interdependence between sequences makes the prediction result of mutation data not accurate. Based on information entropy，a new modified gating weight unit is presented. The change degree of data is quantified by using information entropy to dynamically adjust the weight matrix and describe the trend of data. The experiment is conducted with four public data sets. The experimental results show that the proposed model has better prediction performance than the traditional recurrent neural network.