Abstract: Unlike traditional clustering analysis, the biclustering algorithm works simultaneously on two dimensions of samples (row) and variables (column). In recent years, biclustering methods have been developed rapidly and widely applied in biological data analysis, text clustering, recommendation system and other fields. The traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale data. At present, most of the biclustering algorithms are designed for the differentially expressed big biological data. However, there is little discussion on binary data clustering mining such as miRNA-targeted gene data. Here, we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as GAEBic. GAEBic applies graph autoencoder to capture the similarity of sample sets or variable sets, and takes a new irregular clustering strategy to mine biclusters with excellent generalization. Based on the miRNA-targeted gene data of soybean, we benchmark several different types of the biclustering algorithm, and find that GAEBic performs better than Bimax, Bibit and the Spectral Biclustering algorithm in terms of target gene enrichment. This biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.