With the popularity of cell-phone recording devices and the availability of various powerful and easy to operate digital media editing software, source cell-phone identification has become a hot topic in multimedia forensics, a cell-phone source recognition algorithm based on spectral fusion features was proposed to solve this problem. Firstly, the same speech spectrograms of different cell-phones were analyzed, it was found that the speech spectral characteristics of different cell-phones were different; then the logarithmic spectrum, phase spectrum and information quantity for a speech were researched. Secondly, the three features were connected in series to form the original fusion feature, and the sample feature space was constructed with the original fusion feature of each sample. Finally, the evaluation function CfsSubsetEval of WEKA platform was selected according to the best priority search method to select features, and LibSVM was used to model training and sample recognition after feature selection. Twenty-three popular cell-phone models were evaluated in the experiment, the results showed that the proposed spectral fusion feature has higher identification accuracy for cell-phone brands than spectral single feature and the average identification accuracies achieved 99.96% and 99.91% on TIMIT database and CKC-SD database. In addition, it was compared with the source identification algorithm of Hanilci based on Mel frequency cepstral coefficients, the average identification accuracy was improved by 6.58 and 5.14 percentage points respectively. Therefore, the proposed algorithm can improve the average identification accuracy and effectively reduce the false positives rate of cell-phone source identification.