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Aiming at the poor performance of motor imagery electroencephalogram recognition and the difficulty in modeling complex signals, this paper proposes a motor imagery electroencephalogram recognition method based on a hidden Markov model of multi-time window common spatial pattern. Firstly, the motor imagery electroencephalogram was divided into different time window signals, and then the common spatial pattern was used to extract feature sequences, which can filter out redundant information between electroencephalogram channels. Finally, the forward-backward algorithm and the Viterbi algorithm were used to solve the hidden Markov model and complete classification recognition. The method proposed in this paper was validated on a publicly available motor imagery electroencephalogram dataset, and the classification accuracy rate is 77.17%, which is 5.74% higher than that of the Hidden Markov model algorithm, verifying the effectiveness of the proposed method.