Aiming at the limitations of a single damage indicator for bridge damage identification research, according to the basic principles of wavelet packet transform and neural network principles, the Relative Energy Rate of Wavelet Packet Energy Spectrum (RES) is constructed as a damage identification indicator and an optimised backpropagation (BP) neural network model is established. Backpropagation (BP) neural network model, put forward based on wavelet packet analysis and optimisation of BP neural network bridge structure damage location and damage degree identification method, and the method of experimental validation, to explore the signal noise and speed and other factors on the test results. The results show that: multiple damage conditions are used as the basis for Genetic Algorithm-Back Propagation (GA-BP) and Genetic Algorithm and Simulated Annealing-Back Propagation (GASA-BP). Propagation (GA-BP) and Genetic Algorithm and Simulated Annealing-Back Propagation (GASA-BP) neural networks, the two neural network models show good recognition ability in both numerical simulation and experimental conditions, and in numerical simulation, the maximum average error of GASA-BP is 92.61% higher than that of the GA-BP neural network, and in experimental conditions, the maximum average error of GASA-BP is 92.61% higher than that of the GA-BP neural network. In the experimental condition, the maximum average error of GASA-BP is 67.66% higher than that of GA-BP neural network, which shows that GASA-BP neural network has better recognition accuracy and better robustness. The method requires only a small number of sensors to accurately locate the damage location of the bridge structure.