Xgboost-based whole-genome RNA secondary structure prediction method

The invention provides an Xgboost-based whole-genome RNA secondary structure prediction method. The method includes the following steps that: an RNA sequence and the probability values of the pairingof base sites in the RNA sequence are obtained; a base with a high probability of pairing, and bases of a certain length at the upstream and downstream of the bases with the high probability are combined to form sequence fragments, and the sequence fragments are adopted as positive samples; a base with a low probability of pairing, and bases of a certain length at the upstream and downstream of the bases with the low probability are combined to form sequence fragments, and the sequence fragments are adopted as negative samples; the positive samples and the negative samples are combined into sample data sets, and the sample data sets are divided into a training set and a test set, and the training set and the test set are loaded into a machine learning model established based on the Xgboostalgorithm, and the machin