Scatter-shift ghost imaging edge extraction methods require multiple sampling of the object to obtain a high quality edge map. To solve the problem of many samples and long time when extracting the edge of the object by scatter-shift ghost imaging, convolutional neural network is adopted to the edge extraction experiment of ghost imaging. Firstly, the unknown image is irradiated by Walsh scattering, the sampled signal collected by the barrel detector is input to the ghost imaging edge extraction network as the image feature information, finally the edge information map of the detected object is directly outputted by the trained network, and the output of the convolutional neural network is optimized by using the non-maximum value suppression algorithm. The experimental results show that for the reconstructed object of 128×128 pixels, the signal-to-noise ratio and structural similarity index of the ghost imaging edge extraction network output edge pattern are 5 times and 2 times higher than that of the scatter-shift ghost imaging respectively when the sampling number is 1600, which successfully improves the quality of the ghost imaging edge extraction under the low sampling rate and reduces the sampling time. The ghost imaging edge extraction scheme using convolutional neural network is conducive to fast and high-quality edge detection of ghost imaging in practical applications of object recognition and security inspection.