Freshness is a key index for quality regulation and assurance during the processing and storage of ready-to-eat sea cucumbers. The usual freshness detection methods, sensory evaluation and physicochemical detection, are inadequate for mass standardized and industrial production. In this study, a nondestructive freshness detection method based on hyperspectral imaging was proposed for ready-to-eat sea cucumber (RTESC). The characteristic wavelengths and images were first selected using Principal Component Analysis (PCA) and band ratio algorithm. According to the rottenness mechanism of RTESC, the correlation model between the texture features of hyperspectral images and the freshness degree of RTESC was established to achieve a fast, non-destructive and non-invasive evaluation of RTESC freshness. The effective dimensional-reduction method was adopted to address the massive data of hyperspectral images. According to the spectral absorption characteristics of the sea cucumber body wall, the wavelengths (474 and 985 nm) with significant chemical absorption characteristics were used as dividing points for band division. Thus, five sub-bands and the full band (400～1 000 nm) were acquired for data processing. Next, the bands were optimized using Image Principle Component Analysis (IPCA). Based on the calculated weight coefficients, the band-ratio image at 686 and 985 nm was selected as the characteristic image. On that basis, the gray-gradient co-occurrence matrix (GGCM), gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP) descriptor were constructed to extract texture features. Meanwhile, the measured total volatile basic nitrogen (TVB-N) contents were used as the criterion. Using these three types of texture features as the input data, three freshness evaluation models based on particle swarm optimization (PSO) and back propagation neural network (BPNN) were established. The detection accuracies of these three models are 90%, 95%, and 80%, respectively. The results show that, using the texture characteristics extracted by GGCM from the hyperspectral images, the detection performances are favorable. The present study provides theoretical foundations and technological supports for the development of fast and non-destructive detection methods for RTESC.