A new search scheme utilizing machine-learning methods has been developed to explore the reactions of di-saccharides. It incorporates structure sampling, neural network potential (NNP) training, and target search methodologies, addressing the challenges of their structural diversity and flexibility. We introduce building block sampling to identify transition state (TS) structures and examine the dissociation mechanism of α-maltose under collision-induced dissociation conditions. With a decent NNP model with a mean absolute error of 5 kJ mol