An i-vector speaker recognition method using spectral features was proposed to solve the problem that there is always insufficient information when the mel-frequency cepstrum coefficients (MFCC) are used as feature vectors of i-vectors. Specifically, the speech signals are pre-emphasized, framed and windowed first, and then fed to the short-time Fourier transform to obtain spectrogram. These spectrograms are submitted into Gaussian universal background model for constructing the i-vectors in an appropriate low-dimensional linear subspace flow pattern. These vectors are conformed to normal distribution and reduced by linear discriminant analysis. Finally, Log-likelihood ratio (LLR) method is used for marking i-vectors in training and testing stage to complete the speaker recognition. Standard numerical experiment results with TIMIT database show that compared with recognition method using MFCC as features, the EER(equal error rate) of the method in this paper is lower.