Abstract: An image feature extraction method and apparatus are provided, along with a computer device and a readable storage medium. After obtaining the target convolutional kernel of the feature extraction convolutional neural network, this application will cut the input feature map obtained from the preprocessed image according to the preset image size into multiple abutting input feature submaps. Each input feature submap will be zero-padded to obtain the preprocessed feature submap. Then, for each preprocessed feature submap, the Karatsuba algorithm will be used to reduce the multiplier resource consumption during the convolution operation between the target convolutional kernel and the preprocessed feature submap, effectively suppressing the growth of adder resource consumption during the convolution operation, resulting in the output feature submap. Subsequently, multiple output feature submaps will be overlapped and the boundary of the overlapped feature map will be cropped to obtain the output feature map.