Traditional edge detection methods can not accurately detect the Virtual Boundary (VB) between different regions in materials microscopic images. In order to solve this problem, a virtual boundary detection model based on Convolutional Neural Network (CNN) called Virtual Boundary Net (VBN) was proposed. The VGGNet (Visual Geometry Group Net) deep learning model was simplified, and dropout and Adam algorithms were applied in the training process. An image patch centered on each pixel in the image was extracted as the input, and the class of the image patch was output to decide whether the center pixel belongs to the virtual boundary or not. In the experiment of virtual boundary detection for two kinds of material images, the average detection precision of this method reached 92.5%, and the average recall rate reached 89.5%. The experimental results prove that the VBN can detect the virtual boundary in the image accurately and effectively, which is an alternative method to low-efficient manual analysis.