Molecular bond energy is a key parameter for analyzing the properties of chemical activity, stability and flexibility. Calculating bond energy is a challenge due to the cost of first-principles simulations and unsatisfactory prediction using empirical formula. Here we show that a neural network (NN) machine-learning method can achieve quick prediction of bond energies of organic molecules. Using atomic species and charge information as descriptors, we trained a NN protocol and applied it to predict the bond energy in a certain chemical bond that agreed with density functional theory calculations. This protocol also provided a way to evaluate the effects of different methods of atomic charge analysis on NN training. Trained to accurately estimate bond energies, this NN protocol provides a cost-effective tool for optimizing chemical reactions, accelerating molecular design, and other important applications.