Identifying fish product freshness has always been an important research topic. Compared with the problems of high cost and long detection time of the current conventional fish quality detection methods, hyperspectral imaging technology (HSI) has been obtained due to its non-destructive and rapid advantages. Convolutional neural network is a widely used model in deep learning, with strong expressive ability and high model efficiency. Therefore, a freshness discrimination model of turbot was established using a convolutional neural network (CNN) combined with hyperspectral imaging technology. First, 160 regions of interest (ROI) spectra of turbot samples were collected and divided into 5 categories of freshness according to the samples different freeze-thaw times and freezing times. Based on the VGG11 network, adjust the network structure according to the features of spectral data, reduce the number of fully connected layers, reduce model complexity, and compare the effects of different convolution kernels and activation functions on classification performance to determine the best network framework. Due to hyperspectral data and the large amount of redundant information, Uninformative variable elimination algorithm (UVE) and Random frog algorithm (RF) were used to screen the wavelength of hyperspectral data. The hyperspectral data after wavelength screening were respectively input into a convolutional neural network (CNN), least squares support vector machine (LS-SVM) and K-nearest neighbor algorithm (KNN) to establish the model. Finally, the UVE-CNN model based on the 165 feature wavelengths extracted by Uninformative variable elimination (UVE) has the best discrimination effect, and the accuracy of the classification model on the test set reaches 100%. The results showed that the combination of convolutional neural networks and hyperspectral imaging technology could be used to identify the freshness of turbot effectively. This study provides a new idea for non-destructive and accurate identification of turbot freshness.