Because conventional morphological indicators such as volume and surface area are too general for the subcortical nuclei， it is difficult to detect the subtle changes in the surface morphology using traditional morphological feature acquisition methods. To solve this problem， we propose a fine feature extraction algorithm for subcortical nuclei and apply it to the cognitive state prediction task of the elderly. Using surface conformal parameterization， surface conformal representation， and the surface fluid registration based on mutual information， 15 000×2 morphological features are extracted from both the bilateral hippocampus and amygdala of 46 subjects. Using the dimensionality reduction process， including patch selection， sparse coding and dictionary learning， and max-pooling， we avoid the dimensionality curse while fully preserving the texture information of nuclei. Finally， taking tree as the weak learner， we integrate the final strong classifier using the GentleBoost algorithm for cognitive prediction. The results show that the prediction accuracy of 85% could be achieved only by the novel features of the hippocampus and amygdala， providing a new way perspective for fine feature mining of subcortical structures.