[Purpose/significance] Image retrieval based on user emotion has become a hotspot in machine learning research. However, the corpus data of image sentiment feature annotation is mostly derived from the extraction of low-level features of images, which leads to the simplification and stylization of image retrieval process. The algorithm of image emotion feature extraction based on deep learning fuses the underlying features of the image into the high-level emotion semantics of the image, which provides a reference for the emotional semantic retrieval of images.[Method/process] Using the improved convolutional network model, the color and texture of the dataset image were taken as input, the emotion information of the image was automatically extracted by multi-layer operation, and the sentiment retrieval accuracy of the improved model was calculated though the back propagation algorithm, and an image sentiment feature extraction model with high rate and low degree of over-fitting was constructed.[Result/conclusion] This paper completes the extraction of emotional features of the image through an improved deep convolutional network model, and improves the retrieval accuracy by 10%.
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