The suitability of hyperspectral imaging technology combined with chemometrics was investigated to determine the moisture content of dried oysters. The moisture content not only affects the storage of scallops， but also is closely related to the quality characteristics such as texture and texture. In this experiment， a hyperspectral imaging system was used to collect hyperspectral images in the range of 400 to 1 100 nm. A total of 100 oysters samples of hyperspectral images in 5 different drying stages were collected. The average spectral data of all the regions of interest of the sample are extracted， and the original spectral data is subjected to multi-dimensional scatter correction （MSC） and convolution smoothing （S-G） for preprocessing， and eight characteristic wavelengths are extracted by a correlation coefficient method. Based on the extracted characteristic wavelengths， multivariate linear regression（MLR） and BP neural network models of spectral data and moisture content were established. The results show that both models have achieved good prediction results. The correlation coefficient between the correction set， prediction set and cross-validation set of MLR model are lower than that of BP neural network， but from the analysis of RMSEC， RMSEP and RMSECV， the effect of BP neural network are better than that of MLR. The results show that hyperspectral imaging techniques combined with chemometric methods can be used to detect the moisture content of oysters during drying.