  The Human Genome Project has led to an exponential increase in data related
to the sequence, structure, and function of biomolecules. Bioinformatics is an
interdisciplinary research field that primarily uses computational methods to
analyze large amounts of biological macromolecule data. Its goal is to discover
hidden biological patterns and related information. Furthermore, analysing
additional relevant information can enhance the study of biological operating
mechanisms. This paper discusses the fundamental concepts of RNA, RNA secondary
structure, and its prediction.Subsequently, the application of machine learning
technologies in predicting the structure of biological macromolecules is
explored. This chapter describes the relevant knowledge of algorithms and
computational complexity and presents a RNA tertiary structure prediction
algorithm based on ResNet. To address the issue of the current scoring
function's unsuitability for long RNA, a scoring model based on ResNet is
proposed, and a structure prediction algorithm is designed. The chapter
concludes by presenting some open and interesting challenges in the field of
RNA tertiary structure prediction.
