We used the active learning method to construct training data for predicting DNA hybridization free energies. A total of 64 duplexes were randomly selected from the above pool as the initial training data, including 32 perfect duplexes and 32 duplexes with dangles. After obtaining hybridization free energies of these duplexes, they were randomly divided into train set and validation set in a ratio of 8:2. The same train set was used to train four models with different initialization of weight parameters ω, the corresponding validation set was used to identify the best weight parameter of the model. After training, the model ensemble was obtained, which was used to predict hybridization free energies of remaining duplexes in the pool. The maximal standard deviation of values predicted by the model ensemble is defined as the error indicator ϵ, i.e.: