Integration and Analysis of Biodiversity Data Based on Graph Neural Network

This article introduces a method for integrating and analyzing biodiversity data based on Graph Neural Networks (GNN), aiming to address the limitations of traditional data analysis methods when dealing with highly complex and heterogeneous biodiversity data. In this study, collected data were first preprocessed and feature extraction was performed, followed by the construction of a multilayer heterogeneous graph containing entities such as species, genes, and ecosystems. Building on this, a Heterogeneous Graph Attention Network (HGAT) model was adopted to learn the mutual influences between different data sources and their overall contribution to biodiversity. Experimental results show that the HGAT model achieved significantly better performance than traditional methods in species classification tasks, with an accuracy rate of 89.2 %, F1 score of 0.885, and ROC-AUC of 0.932. Additionally, by analyzing species diversity and interaction networks, the stability and health status of ecosystems were evaluated, and key species crucial for ecological network balance were identified. This research not only improves prediction accuracy but also enhances the interpretability of the model, providing new perspectives and tools for the conservation and management of biodiversity.