In cross-corpus speech emotion recognition， the mismatch between target domain and source domain samples leads to poor performance of emotion recognition. In order to improve the cross-corpus speech emotion recognition performance， this paper proposes a cross-corpus speech emotion recognition method based on deep domain adaptation and convolutional neural network （CNN） decision tree model. Firstly， a local feature transfer learning network based on joint constrained deep domain adaptation is constructed. By minimizing the joint difference between the target and source domains in the feature space and Hilbert space， the correlation between the two corpora is mined and the transferable invariant features from the target domain to the source domain are learned. Then， in order to reduce the classification error of confusable emotions among multiple emotions in the cross-corpus context， a CNN decision tree multi-level classification model is constructed based on the emotional confusion degree， and multiple emotions are first coarsely classified and then finely classified. The experiments are validated using three corpora， CASIA， EMO-DB and RAVDESS. The results show that the average recognition rate of the proposed cross-corpus speech emotion recognition method are 19.32%—31.08% higher than that of CNN baseline method， and the system performance is greatly improved.