Alzheimer's disease (AD) has become one of the most serious healthcare problems. As a result, early diagnosis for AD is important for intervention of its deterioration at an early stage. Diffusion tensor imaging (DTI) provides a non-intrusive examination of cranial nerve diseases, but the precise quantification is a problem for diagnosis. In current study, we proposed an AD recognition method based on quantitative analysis and data fusion of T1 and DTI images. Through segmentation on T1 image, feature extraction of DTI data, and data fusion based on multi-features, recognition accuracy of AD can attain as high as 95.4%. For details, patterns of different features from DTI data were investigated showing that the fusion of volume values, DTI and diffusion kurtosis imaging (DKI) parameters and the Gaussian Mixture Model (GMM) parameters of DTI and DKI are the most distinctive individual features in AD patients. It also implies that during the AD development, neural degeneration could be demyelination, reduction of structural complexity and decrease of brain microstructure connectivity, which can be reflected by different quantitative parameters from brain images, such as brain structure volume, DTI parameters and DKI parameters. Further study on the image features, including more image types, would provide valued method for AD screening or early diagnosis.