The invention discloses a bridge damage identification method based on a neural network. The method includes the following steps of firstly, constructing sample data, wherein a bridge model is established with a finite element method, simulation strain data are obtained under the condition that a bridge is complete and under the condition that the bridge is differently damaged, and the strain change rates serve as the sample data of the BP neural network; secondly, determining a network topology structure, wherein the number of hidden layers of the BP neural network and the number of nerve cells contained on each layer are determined, and meanwhile the weight threshold value of the neural network is initialized; thirdly, conducting training and testing, wherein the BP neural network is trained through a gradient descent momentum algorithm, and the neural network is tested through a testing sample; fourthly, identifying the damage, wherein the damage of the bridge is identified by inputting the real-time train data of the bridge into the trained BP neural network. The bridge is identified through stress parameters, and therefore bridge damage identification accuracy is improved.