Morphological characteristics of new media platform based on standard template library algorithm discretization

Abstract

The research aims to develop a triangular grid adaptive algorithm to analyze the morphological features of new media platforms by means of a standard template library algorithm. The purpose of the study is to improve the accuracy and efficiency of morphological characterization of new media platforms, and to provide theoretical support for new media content recommendation and user experience optimization. The experimental results show that the triangular lattice adaptive algorithm performs well in terms of accuracy and efficiency, with an average accuracy of 97.71%, and the optimized working time is significantly reduced to 77 minutes. In contrast, the random forest algorithm, the back propagation neural network algorithm and the artistic fish school algorithm have an average accuracy of 96.12%, 95.80%, and 95.92%, with a working time of 80, 87, and 84 minutes, respectively. The triangular grid adaptive algorithm not only has advantages in accuracy and efficiency, but also has the advantages of versatility, less time consumption and economic cost saving, which makes it suitable for analyzing the morphological characteristics of new media platforms.