A method for selecting parameters in HgCdTe crystals has been proposed, utilizing Principal Component Analysis (PCA) and clustering methods, with the establishment of a data model for screening the parameters of HgCdTe crystals. Within the model, initial crystal data undergoes a cleaning and analysis process. PCA is employed for dimensionality reduction, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to identify the densest regions within the crystal data. Furthermore, high-performance chip data, obtained post-processing, is utilized to fit boundary ellipses for high-quality HgCdTe crystal parameters. These ellipses act as criteria for identifying high-quality crystals. Capable of generating crystal ratings based on input electrical and optical parameters, the model achieves a coverage rate exceeding 90%.