Domain adaptation method using residual attention module

The invention provides a domain adaptation method using a residual attention module. The method comprises the following steps: selecting pictures with relatively large energy difference in a target data set as an activated picture data set through an energy function; taking the data set marked by the source domain, the data set unmarked by the target domain and the activated picture data set as input data, inputting the input data into a feature extraction network comprising a convolution and attention module and an LSEAt structure, and extracting a high-dimensional feature map of the input data by the feature extraction network; and mapping the high-dimensional feature map into a one-dimensional feature vector through a full-connection layer classifier to realize image classification, and forming an image recognition model by using the feature extraction network with adjusted parameters and the full-connection layer classifier. According to the method, feature extraction is deepened by increasing participati