For overcoming defects in prior art, the invention aims to perform identification on the circRNA by means of a lightGBM method. The method comprises the following main steps of (1), inputting the circRNA of a big data sample in a (.bed) file form, wherein the circRNA comprises a chromosome number, a sequence starting site and a positive-and-negative chain mark; (2), mapping the circRNA(.bed) file to a human whole genome (hg19 version) according to the related information of starting site, and obtaining a circRNA sequence information (.fasta) file; (3), presenting a characteristic fusion algorithm for extracting the related characteristic of a circRNA forming process; (4), according to a lightGBM algorithm, respectively performing sample data sampling and characteristic sampling by means of a GOSS and EFB, mapping the continuous characteristics into discrete buckets by means of a Histogram-based algorithm, forming multiple bins, and establishing a histogram by means of the multiple bins, namely discretizing a continuous variable; and (5), adjusting parameters such as a maximal depth max_depth of the tree, a possible minimal recording number min_data_in_leaf of the leaf, and a data proportion bagging_fraction in each iteration, thereby obtaining a model optimal parameter.
LightGBM-based circRNA identification method