In order to accurately obtain the global characteristics of motor imaging EEG signals and the common characteristics between individuals, and then improve its classification accuracy and model robustness, a fusion convolutional neural network algorithm with parameter sharing transfer learning is proposed. First, the trained model on the source domain is migrated layer by layer to the target network to obtain the best migration layers. Secondly, after the migration layers, different numbers of convolution-pooling blocks are connected to form four convolutional networks with different depths, and they are merged in parallel and finally the classification results are obtained through the classifier. Use the BCI competition IV Datasets 2a to conduct experimental analysis on the proposed method. The results show that the average recognition rate of all subjects when using 100% and 50% samples is 80. 85% and 78. 9%, respectively, which verifies the effectiveness of the proposed method on global feature extraction and the advantages of small sample problems.