The geometry of uranium components is one of the key characteristics and strictly confidential. The geometry identification of metal uranium components was studied using 252Cf source-driven correlation measurement method. For the 3 uranium samples with the same mass and enrichment, there are subtle differences in neutron signals. Even worse, the correlation functions were disturbed by scatter neutrons and include “accidental” coincidence, which is not conductive to the geometry identification. In this paper, we proposed an identification method combining principal component analysis and least-square support vector machine (PCA-LSSVM). The results based on PCA-LSSVM showed that the training precision was 100% and the test precision was 95.83% of the identification model. The total precision of the identification model was 98.41%, which indicated that the identification model was an effective way to identify the geometry properties with the correlation functions.