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In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self-designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA-based systems., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)