The invention provides a method for realizing a multi-channel convolutional recurrent neural network EEG emotion recognition model using transfer learning, the method uses a dual-channel one-dimensional convolutional neural network model constructed based on three heartbeats recognition method as the source domain model for transferring, to obtain a multi-channel convolutional recurrent neural network EEG emotion recognition model with EEG signal as the target domain, it solves the problem of scarcity of EEG labeling data, and can improve the accuracy of EEG emotion prediction. The accuracy of data processing is improved by decomposing and normalizing the EEG data set; the transferred multi-channel convolutional neural network extracts the features of multi-channel EEG signals in EEG data set; combined with the recurrent neural network, sequence modeling is carried out to extract multi-channel fused emotional information; the feature redistribution is realized by adaptive attention model and weighted feature fusion, and the complete feature tensor is obtained.