Abstract: The inner product matrix constructed by time domain vibration response under ambient excitation is a good structural characteristic parameter in structural health monitoring. In order to improve the identification accuracy of the structural health monitoring method using inner product matrix, more vibration response measurement points are often needed, which will directly affect the engineering practicability of the method. Based on the correlation analysis theory of time domain vibration responses, the inner product matrix is extended to the correlation function matrix to obtain more structural health characteristic information from a small number of vibration response measurement points, and the requirement of the number of measurement points for the structural health monitoring method will be reduced. Furthermore, combining with the excellent data feature extraction capability of convolutional neural network, a structural health monitoring method based on correlation function matrix and convolutional neural network is proposed with correlation function matrix as input and structural health status as output. The experimental results of the bolt loosening monitoring of a typical aeronautical stiffened panel show that the identification accuracy of the proposed method for bolt loose position can reach more than 99% by using only the time domain vibration responses of any two measurement points.