In order to improve the space resolution of hyper-spectral image by fusing the spatial information of multispectral images and the spectral information of hyperspectral images, a hyperspectral image super-resolution algorithm based on relevance vector machine (RVM) is proposed. A brief introduction of the principle of the Price method which fuses multispectral and hyperspectral images to get the super-resolution image is given, and the RVM linear regression is introduced. Combining with the advantages of RVM in regression analysis, a resolution enhancement by revealing the corrspondence of the spatial and spectral information is gotten. The experiment results show that the normalized root-mean-square (RMS) is lower than 0.001 and the spectral angel error is lower than 0.02, which gets a great improvement compared with the results of the Price method and the Elbakary method. The method proposed has a significant result in hyperspectral image reconstruction, which provides a much properer data source for classification, object detection and recognition.