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Traditional recognition methods of motor imagery electroencephalogram (EEG) need to extract a lot of features artificially, and the recognition performance is greatly affected by the experience of researchers and has strong subjectivity. In this paper, an automatic recognition method of motor EEG signals based on deep learning is proposed. Firstly, the time-domain EEG is transformed into amplitude phase domain two-dimensional images by Hilbert transform, and then the convolution neural network is used to extract features and recognize different EEG signals. The experimental results based on Graz data set used in BCI competition show that the proposed algorithm can achieve better performance than the traditional feature extraction methods It has better recognition performance, and has higher robustness under the condition of low signal-to-noise ratio.