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In order to solve the problem of low classification accuracy in motor imagination brain-computer interface system, an improved EEG signal classification method based on siamese network was proposed. Two subnetworks in the original siamese network were expanded into three subnetworks, and a new learning sample collection method and distance function were designed. After wavelet transform and empirical mode decomposition, EEG signals are screened by auto-correlation function threshold to obtain the pre-processed wavelet component. Then it is divided randomly into training set and test set, and the learning sample set is obtained from the training set according to the new learning sample collection method, and the learning sample set is input into three sub-networks with shared weights for training, and the new distance function is used to compare the similarity. Finally, the feature similarity between the test sample features and the samples labeled 1 and 0 in the training set is calculated, and the sample label with the highest similarity is selected as the category of the samples to be tested. Through The simulation of international open BCI Competition II Data Set III and The largest SCP Data of motor-imagery Data set, the classification accuracy of this algorithm is up to 94.29%.Compared with the existing algorithms with higher performance, it effectively improves the classification accuracy and can better classify and recognize eeg signals.