Moisture content had a close relationship with the microbial growth and therefore it profoundly affected the dried scallop quality and safety, which is harmful to human health. In this study, a hyperspectral imaging system in the 380~1 030 nm was used for rapid detection of the moisture content of scallops. 90 hyperspectral images of six different dehydration periods were obtained. The mean spectra value of scallops from hyperspectral images were extracted and arranged in a matrix. Successive Projection Algorithm (SPA) and Weighted Regression Coefficient (Bw) were used to establish Partial Least Squares Regression (PLSR) models to correlate the spectral feature with moisture content. Seven and four optimal wavelengths were selected respectively to develop new simplified models called SPA-PLSR and Bw-PLSR, which brought about sound prediction results (correlation coefficients were higher than 0.95, the root mean square error were less than 10%). Then the best model called SPA-PLSR with less wavelength variables and higher prediction of 97.28%, was applied to create visualization map to observe the distribution of moisture content by pseudo color image programming technology. The results revealed the feasibility of hyperspectral imaging technique combined with the optimal wavelengths for estimating and visualizing the moisture content of scallops.