With the coming of intelligent era, deep learning algorithm has been applied more and more widely in the field of image processing, which brings new solutions to complex image processing. However, while deep learning can produce highly accurate inference results, it often leads to computational complexity, the increase of inference time and processor memory. Due to the lack of data and reliance on high-performance processors, deep learning related products have not been widely used in real life scenarios. Based on the above questions, this paper proposes a multi-category image recognition scheme based on deep learning with broad application prospects and carries out test verification under the Loongson platform. Firstly, the image data set of the category to be identified is obtained, and then the neural network model is built and trained under the computer platform. The optimized neural network structure is established under the Loongson platform using the obtained training parameters, and then the target image is processed and recognized. Finally, the category of the target image is displayed in the user interface. The system utilizes the advantages of deep learning to automatically learn big data features and realizes automatic classification of thousands of common objects in life under the Loongson platform. The recognition accuracy can reach more than 96%, and the recognition time is limited to 3s or less. The scheme also demonstrates excellent scalability.