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Quick and accurate assessment of fish freshness is of great significance for intelligent quality monitoring and ensuring the safety of consumers. In the current fish freshness evaluation method based on visual images, the study of fish gills needs to remove the gill cover, which is invasive to the fish body, and the analysis of other parts has a low evaluation accuracy. To solve the above problems, a fish freshness classification method based on color histogram & grey-level co-occurrence matrix-linear discriminant analysis (CHG-LDA) was proposed. Firstly, preprocessing operations such as labeling, image zooming and color space conversion were performed on the collected fish images. Secondly, the extracted color histogram features and grey-level co-occurrence matrix (GLCM) features were fused to constitute the features, and the feature dimension was reduced by LDA. Finally, K-nearest neighbor (KNN) algorithm was used to classify fish freshness. The CHG-LDA method proposed solved the problem of poor classification performance caused by the low quality of the extracted fish image features. The experiment was carried out on a real crucian data set, and the index values of precision, recall, F1-score and accuracy were all 1. Compared with color histogram features, color moment, GLCM features, etc., this method improved the performance of each evaluation index on KNN, RF, ANN, and LightGBM classifiers. Among them, the evaluation time of KNN was the best, which was 0.01s. Experimental results showed that this method can achieve accurate and non-destructive evaluation of fish freshness, and it was feasible for actual production monitoring.