A convolutional neural network (CNN) methodology was proposed to automatically interpret stabilization diagrams.Once the stabilization diagrams of different structures had been obtained, they were equally distributed into several frequency bands according to the accuracy requirements of each frequency identification, which were called single mode stabilization diagrams.These frequency bands samples were used as learning samples of the CNN.After that, these learning samples were expanded with some technical methods such as translating and changing the label of stable poles on the stabilization diagram.Then, the preprocessed learning samples were substituted into the constructed CNN.The parameters of CNN, such as learning ratio, were tuned by tracking the changing rule of losing function during the learning process.Finally, a CNN which can automatically eliminate the spurious modes on the stabilization diagram was obtained.The constructed CNN was verified by a 3 degree of freedom(DOF), a 7DOF spring-mass model as well as the accelerometer data of a reinforced concrete frame structure and the Swiss Z24 bridge.The robust learning and prediction results prove that the constructed CNN is effective for analyzing any stabilization diagram of different structures.It can automatically and accurately eliminate the spurious modes on the stabilization diagram immediately without  extracting any characteristic parameters or setting any thresholds of them.