In order to improve the accuracy of pork freshness identification, a method for pork freshness identification based on improved residual network and transfer learning was proposed. First of all, the pork freshness was classified into seven grades, according to the aerobic plate count, coliform bacteria and pH value of pork combined with national pork food standards（national standards）. The ResNet-50 model was trained with the PfidSet data set to have the ability of extracting image features. Then, the ResNet-50 model was improved by using model transferring and model finetuning in the following ways: firstly, replacing the full connection and classification layers of the ResNet-50 model with a 3layer adaptive network; next, initializing the improved ResNet-50 model weights by using the network parameters trained on the PfidSet; then using LReLu-Softplus as the activation function of the adaptive network; finally, transferring the knowledge gained by the improved ResNet-50 model on the image data set of the pork sample to the task of Iberico pork freshness identification. A total of 23427 images were selected to form the sample set. Then, 80% of the samples were randomly selected from the sample set to be used as the training set, and the remaining 20% for the test set. The test results showed that transfer learning could significantly improve the convergence speed and classification performance of the model, and data augmentation could increase the diversity of data, avoiding overfitting phenomena. The accuracy of classification in transfer learning and data augmentation could reach as high as 94.5%. Moreover, the test method was an efficient method for classifying pork freshness.