Abstract: Traditional methods classify agricultural products by fined-grained level and determine the key factors affecting the classification effect,but ignore the quality characteristics of agricultural products.Scientific classification of agricultural products quality can not only effectively improve the speed of subsequent processing of agricultural products,but also better reflect the changes in the quality of agricultural products.Starting from the quality characteristics of agricultural products,agricultural pro-ducts are classified,and different types of agricultural products are processed in different methods,so as to ensure the quality of agricultural products and increase their added values.The classification method and the selection of model parameters are especially important for the accuracy of agricultural product quality classification.Traditional support vector machine (SVM) has blindness in the selection of model parameters.In order to improve the classification accuracy of agricultural product quality,a product quality classification model combining factor analysis (FA) and improved support vector machine (GA-SVM) is proposed.Experimental results show that the improved SVM can quickly and effectively identify the quality categories of agricultural products,significantly improve the classification accuracy of agricultural product quality.The evaluation process is relatively simple and can be widely applied to the evaluation of agricultural product quality.

Key words: Agricultural products, Factor analysis, Genetic algorithm, Quality classification, SVM