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With the aim to solve the problem of manual sampling and sensory identification of feed raw material entering the silo in the feed production process, and realize automatic identification of raw material type, taking bulk feed raw material such as corn, bran, wheat, soybean meal and fish meal as the research object, a multi-channel automatic identification device for feed raw material type was designed and built independently, feed raw material image dataset was collected, and data augmentation methods were used to increase sample diversity. Based on ResNet18 convolution neural network, CAM-ResNet18 network model for feed raw material type identification was constructed by adding the channel attention mechanism, adding the Dropout method, adopting the Adam optimizer and embedding the cosine annealing method，while the migration learning was introduced to train the model. The average accuracy of the CAM-ResNet18 network model for feed raw material type reached 99.1% in the validation set, with a recognition time of 2.58ms. Compared with the ResNet18, ResNet34, AlexNet and VGG16 network models, the validation accuracy was improved by 0.6, 0.2, 3.7 and 1.1 percentage points, respectively. For the result analysis of confusion matrix, the average accuracy of test set recognition was 99.4%, which had high accuracy and recall. The results showed that CAM-ResNet18 network model had higher accuracy rate and faster detection speed in the identification of feed raw material, providing a theoretical method and technical support for the identification of feed raw material entering the silo in the actual production.