The invention relates to the field of graph classification and recognition, in particular to an image classification and recognition method and device based on a self-adaptive dynamic convolutional network and computer equipment, and the method comprises the steps: obtaining a to-be-detected image, inputting the to-be-detected image into a preprocessing block, and obtaining a shallow feature graph and graph parameter information of the image; combining the image parameter information obtained after preprocessing with the to-be-detected image, and inputting the combined image parameter information and the to-be-detected image into an adaptive dynamic convolutional network of a backbone network to obtain image global features; wherein the adaptive dynamic convolution is to select a convolution kernel with a corresponding shape according to the corresponding parameter information; inputting an image shallow layer feature map obtained after preprocessing into a branch network, and extracting local features of the to-be-detected image; and carrying out feature fusion on the local features and the global features, inputting the fused features into a classification network, and outputting classification identification information of the to-be-detected image. According to the method, the calculation cost required for image classification and recognition is low, the precision is high, and the applicability of related products is high.