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Due to EEG recognition accuracy was relatively low in the case of multi-class problem of motor imagery,this paper presents a method that a new combination about wavelet packet variance (WPV), wavelet EEG feature package entropy (wavelet packet entropy, WPE) and common spatial patterns (CSP) extract features based on wavelet packet specific frequency bands, which input into support vector machine(SVM ) classifier achieve resultant classification. Firstly, selecting the EEG of important channels make wavelet packet de-noising and decomposition(wavelet packet decomposition,WPD) of specific frequency bands; Secondly, optimization of important channels calculate the wavelet packet variance (WPV) and wavelet packet entropy(WPE) as feature vectors; then, the three sub-band coefficients for each channel EEG signal of important channels are reconstructed and feature extraction carried by common space pattern(CSP); Finally, two kinds of feature vectors from different ways that feed into a classifier and achieve classification. The highest classification accuracy rate of 88.75%, comparing with the relative two kinds of single-extraction method increased 28.27% and 6.55% by l1b from BCI2005desc_IIIa.The results show that the algorithm can effectively extract the feature vectors, thereby improving the lower classification accuracy problems.