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Fish parasites adversely affect aquaculture and can be transmitted to humans via the food chain, necessitatingrapid, non-destructive detection methods. This research investigated the use of visible/near-infrared (VIS/NIR) hyperspectral imaging for the nondestructive identification of Clonorchis sinensis infected Pseudorasbora parva fish, with a 31.82% infection rate in the wild in China. Our analysis of various preprocessing, feature extraction, and modeling algorithm combinations revealed that using standard normal variate preprocessing, competitive adaptive reweighted sampling for feature selection, and partial least squares discriminant analysis significantly enhanced detection efficacy. Detection accuracies reached 99.24% and 93.93% for calibration and validation sets, respectively, based on the spectrum of whole fish. Detection accuracy varied among different fish parts, with the highest in the stomach, followed by the head, caudal fin, and back. Specifically, Stomach spectra achieved 99.24% and 93.93% accuracy for calibration and validation sets, respectively. Notably, a single local spectrum from a 15 × 15 pixel-sized area of the stomach detected parasites with 99.99% and 90.90% accuracy for calibration and validation sets, respectively, significantly increasing detection speed. This study provides a comprehensive methodology for the rapid, nondestructive detection of fish parasites via VIS/NIR hyperspectral imaging technology, demonstrating significant potential for application. • The most effective method for detecting fish parasites was SNV + CARS + PLS_DA. • Stomach was the optimal feature extraction component for identifying fish parasites. • The pixel size for extracting the most minor spectrum for detection was 15 × 15. • VIS/NIR hyperspectral imaging was an efficient tool for detecting parasites in fish. [ABSTRACT FROM AUTHOR]