Design and Implementation of Automatic Generation Algorithm for Advertising Artistic Design Based on Neural Networks

Abstract

The traditional computer-aided design (CAD) software is often not realistic in rendering scenes, such as light, materials, shadows, and other details, which leads designers to spend a lot of energy on manual adjustment. The rendering algorithm based on deep learning (DL) can automatically learn the complex output, and the effect is usually better than traditional methods. In this article, a convolutional neural network (GA-CNN) based on genetic algorithm (GA) optimization is proposed, and it is combined with CAD data and applied to the rendering of environmental art design scenes. This method uses GA to optimize the parameters of CNN to improve its learning effect and generalization ability. From the overall trend, the response speed of GA-CNN algorithm and support vector machine (SVM). The response speed of the traditional CNN method is relatively slow, and the fluctuation range is large. This is because traditional CNN needs a lot of convolution operations and feature extraction when dealing with artistic scenes in complex environments, which leads to a long calculation time. In practical application, it is necessary to select appropriate algorithms and parameter settings according to specific task requirements to achieve the best results.