This article explores the application of machine learning algorithms in the analysis of biological material structures. Biomaterials have attracted much attention due to their unique properties and broad application prospects, but their complexity and diversity make traditional research methods difficult to meet current needs. Machine learning algorithms, as a powerful data analysis tool, can play an important role in the structural analysis of biomaterials. This article first provides an overview of existing machine learning algorithms and their current applications in biomaterials science. Then, it provides a detailed introduction to the specific applications of machine learning algorithms in structural analysis of biomaterials, including feature extraction, structural classification, performance prediction, and optimization design. The final experimental results show that the overlap rate between the results of the biological material analysis conducted by the algorithm in this paper and the actual material structure is extremely high, within the range of [0.9, 1]. And compared with other machine learning algorithms, this algorithm has the best performance, with an F1 value of 0.98 and a recall rate of 97.67%.