Application of Regional Symbol Recognition and Collaborative Design Based on Deep Learning in Cultural Heritage

The traditional advertising design mode can not meet the increasing competition in the advertising industry on the demand for personalization and efficiency; designers, in a short period of time, can not quickly accumulate design experience and materials to improve the efficiency and quality of advertising creative design.Therefore, this paper combines a convolutional neural network and Teamcenter to construct an intelligent advertising creative generation model based on computer-aided design (CAD) and collaborative design to realize the automation and personalization of advertising design.The experimental results show that the model has a fast performance convergence speed and high accuracy and can effectively classify elements and ensure high accuracy.At the same time, the extracted creative elements have the lowest error, which is more in line with the aesthetic needs of designers and customers, and it can provide effective and accurate data information for the generation of advertising creativity.In addition, compared with the other two creative generation models, this model can keep a relatively short time in generating creative ideas of different difficulties and obtains the highest degree of recognition from designers and clients.