To solve the problem that novelty detection is difficult to identify the time and location of structural damage at the same time, convolutional neural network was introduced into novelty detection. Firstly, the wavelet packet technology was used to process the structural response to obtain the wavelet packet energy, and the energy ratio of the corresponding frequency bands of adjacent measurement points was applied as the feature vector of the novelty detection model. Then, taking the eigenvector of the healthy structure as the training data, the novelty detection model based on convolutional neural network under the health mode was established. Next, the real-time output feature vectors of the structure was input into the novelty detection model, the output with was compared the output of the health state, and the Euclidean distance between the output and input was used as the novelty index. Finally, the time and location of structural damage based on changes were identified in novelty index. The effectiveness of the method herein were verified through numerical simulation and laboratory experiments.