Application of Few-Shot Learning Algorithms for Data Stream Analysis in the Development of Dance Trends on Digital Platforms

Information visualization in multimedia production of dance situations on digital platforms focuses on information abstraction, transforming complex visual structures such as images and words into abstract data structures. Based on the small sample learning algorithm, this paper studies the development of dance situations on digital platforms through data flow analysis. The research shows that the error rate of this algorithm in the initial and incremental stages is significantly higher than other algorithms. At the initial stage, when the number of iterations reaches 100, the error rate of this algorithm is 11.1%, while that of the data mining (DM) algorithm is 26.9%, and the AI algorithm is 37.4%. Compared to traditional point integrators used in other methods, it can be concluded that this algorithm outperforms the other two. In each iteration of model training for digital platform dance situations, the small sample learning algorithm minimizes prediction loss on the query set using information from the support set.