Abstract: Huizhou architecture comprises one of the four ancient architectural schools in China, with wood components being its core. The accurate prediction of Huizhou architectures wood life is of great significance for the protection of ancient buildings. At present, there are few studies have been conducted on the influence of various factors on the service life of the wood components. Elman neural network is typical multi-layer dynamic recurrent neural network, which has the function of mapping dynamic characteristics by storing internal state. This gives the network the ability to adapt to time-varying characteristics, which can be used to predict the complex nonlinear time-varying system. The basic Elman neural network has the characteristics of slow training speed and the tendency to fall into local minimums. Therefore the particle swarm optimization algorithm with adaptive mutation operator is used to improve the basic Elman neural network. The algorithm optimizes the weights of each layer in the network, improves the learning speed, and finds the optimal solution in the global range. The improved network can fit the training value more accurately and can effectively predict the test value. The simulation results show that the network structure can be well applied to the life span prediction of Huizhou architecture.