In summary, the accurate prediction of DNA hybridization free energies and DNA strand displacement rate constants was achieved by combining quantum chemistry calculations with deep learning models. To comprehensively describe energy variations, the nearest neighbor model's 16 stacks were expanded to 400. Their energy values were obtained through quantum chemistry calculations, while their entropy was predicted using a physically motivated statistical model. Additionally, DNA strands were preprocessed into descriptor matrices and served as inputs for a convolutional neural network. For training data selection, an active learning method was employed on a pool of 20,000 DNA duplexes, resulting in the selection of 196 duplexes. This approach significantly reduced the number of required labeled samples. Notably, our model outperformed the nearest neighbor model by 20.3% in predicting hybridization free energies on a test set of 48 duplexes. Similarly, employing the active learning method, a pool of 10,000 sequence sets of DNA strand displacement reactions was constructed, and 268 reactions were selected as training data. The accuracy of our model in predicting strand displacement rate constants demonstrated similarity to a recurrent neural networkbased deep learning model on a test set of 81 reactions. With the rapid development of synthetic biology and DNA computing, we believe that our model will be instrumental in the design of engineered oligonucleotides with desired thermodynamic and kinetic properties within DNA-based systems.