Abstract
For the information contained in the growth data of the pearl gentian grouper, there is overlap and inclusion. When establishing the growth model of the pearl gentian grouper, it is difficult to grasp the inherent laws of the growth factors to affect the establishment of the research target model and the operation efficiency. The data dimensionality reduction method is often used in the data preprocessing stage. The Principal Components Analysis (PCA) method was used to analyze and influence the growth factors of the pearl gentian grouper The method of regression fitting optimizes the results of selecting principal components under different criteria. Experimental results show that the main growth factors of the pearl gentian grouper selected by this method can meet the needs of the construction of aquaculture growth models, and can provide a reference for reducing the dimensions of aquaculture fish growth data and the complexity of the modeling process.
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