Based on the Visual Geometry Group (VGG-19) model of convolutional neural networks (CNN), influences of the edge information in an input texture feature map on the natural texture are studied when the CNN convolves the input texture. When the input image is convoluted by the VGG using the CNN, the feature map is processed in an average pooling manner to prevent overfitting, which protects the edge information of the feature map to some extent and the generation effect is better than that obtained via max-pooling processing. The edge information of each layer of feature map is extracted and superimposed on the feature map, which preserves the edge structure information of the texture image well. Experimental results demonstrate that the proposed method achieves a good texture generation effect.