The rapid development of modern technology and civilization has made the survival of non-heritage culture more and more serious, and the protection and inheritance of intangible cultural heritage is a heavy task and a long way to go. This paper takes the feasibility of non-heritage animation creation as an entry point, analyzes the ideological mechanism in the process of non-heritage animation creation, and explores the economic realization brought by deep learning technology assisting non-heritage animation creation. For the lens scene switching in the process of non-heritage animation creation, this paper utilizes the CNN network for the initial positioning of the lens boundary. It establishes the tangent detection model of a non-heritage animation lens by combining it with the 3D-CNN network. To understand the diversity of non-heritage animation creation styles, this paper establishes a model for style migration of non-heritage cultural images based on the VGG-Net network and conducts experimental investigations. The results show that when the hyperparameter value of the model is set to