The traning and testing data for speech emotion recognition often come from different corpora.In this case，the mode recognition performance decreases greatly due to the domain mismatch.To address this problem， we present a new composition method using graph convolutional network to represent the topological structure between the source and target databases for cross corpus speech emotion recognition. Besides，aiming at the problem of low accuracy of single feature in emotion recognition，a novel feature fusion method is proposed.Firstly， we extract the acoustic features by OpenSMILE， then extract deep features by graph convolutional neural network. With the proceeding of convolutional layers，nodes transmit the feature information to another nodes，making the deep features contain clearer feature information and more detailed semantic information. Finally， we fusion the shallow and deep features. Two classification experiments are carried out. eNTERFACE corpus is for training and Berlin corpus is for testing， and the recognition rate is 59.375%. Berlin corpus is for training and eNTERFACE corpus is for testing， and the recognition rate is 36.111%. The experimental results are higher than the best research results in the baseline system and references， which proves the effectiveness of the method proposed in this paper.