The invention discloses a local feature extraction method based on deep learning, and the method comprises the following steps: carrying out the network training, training a pre-constructed network on an image data set MS-COCO, segmenting the data set into a training set and a verification set which respectively comprise 82783 and 40504 images, carrying out the image matching, in an experiment, using a standard local feature pipeline to evaluate the performance of the local feature extraction method, wherein the standard local feature pipeline is used for extracting and matching features from any given pair of images in an experiment, then carrying out repeated score (Repeatability) calculation, then carrying out matching score (M-Score) calculation, and finally performing homography estimation effect evaluation. The detection step is postponed to description, so that more stable key points are obtained, compared with a traditional non-machine learning mode, the method has a more flexible feature searching process, and feature extraction precision is improved while a large number of key points are obtained.