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For the problem of the individual difference and the classification accuracy of multi class motor imagery EEG signal, a new analysis method for EEG signal based on time-space- frequency domain is put forward:firstly, the wavelet packet is used to decompose the original signal of EEG, and the motor imagery EEG rhythm is extracted according to the frequency distribution of EEG signal, and the spatial features of EEG are extracted from different motor imagery tasks through the "one-to-rest" common space pattern (CSP) algorithm; then the feature vector is input to the support vector machine (SVM) in "one-to-rest" mode, the output value of SVM is fused via the method of judging the decision function value; finally, the time domain window is used to filter the EEG signals to eliminate the fluctuations of the brain at the beginning and end of motor imagery, and further improve the signal to noise ratio and the classification accuracy of the algorithm. The experimental results show that, when the time window is 2 s, the average maximum coefficient is 0.72, which is 0.15 higher than the first of BCI competition. Meanwhile, the results verify that the algorithm can effectively reduce the influences of the individual differences of EEG signals, and improve the accuracy of multi-class recognition.