Here the active learning method was employed again to construct training data for predicting DNA strand displacement rate constants. A total of 100 sequence sets were randomly selected from the above pool as the initial training data. The threshold of model deviation was set to 0.5, and the top 10 sequence sets with error index greater than the threshold were taken as candidates per iteration. During the iterative process of active learning, as the number of iterations increases, the mean RMSE of the train set gradually rises from the initial 0.239 and eventually reaches 0.317 at equilibrium (Figure ), while the number of candidates steadily decreases from the initial 270 and eventually reaches 0 (Figure ). 81 sequence sets were selected by the active learning method, along with the initial training data, a total of 181 sequence sets were used as the final training data. The test set consisting of 48 sequence sets were randomly generated based on the above sequence design principle, and their strand displacement rate constants were obtained.