In this paper, a new method for the classification of Alzheimer's disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.