A new electroencephalogram （EEG） emotion recognition method based on deep convolutional neural network-joint adaptation network （CNN-JAN） is presented. It incorporates the idea of joint adaptation in transfer learning into deep convolutional networks. Firstly， the model uses a rectangular convolution kernel to extract the deep emotion-related spatial features between EEG data channels. Then， the extracted spatial features are input into the adaptation layer with multi-kernel joint maximum mean discrepancy （MK-JMMD） for transfer learning， aiming to reduce the distribution differences between the source and target domains. The experiments are carried out on differential entropy features and differential causality features of EEG data from the SEED dataset to verify the effectiveness and advantages of the proposed method. As a result， the within-subject emotion classification accuracy on differential entropy features reaches 84.01%， and the cross-subject emotion classification accuracy is also improved compared with other current popular transfer learning methods.