In order to effectively and accurately diagnosis the damage of cable stayed bridge, the damage diagnosis method of cable stayed bridge based on pattern recognition was studied. The low order modal frequency and vertical vibrational mode of some key points were selected for dynamic fingerprints of no modal expansion or model condensation. The full factorial design was used to create the dynamic fingerprint database, which could accurately evaluate the damage factors and their interaction effects on the damage identification results. And the dynamic fingerprint database with random error was designed and added. The pattern recognition algorithms based on MATLAB were compiled. The high accuracy of the multilayer perceptron recognition algorithm and the algorithm to improve the prediction accuracy of the bagging ensemble algorithm were mainly studied. In the end, the damage diagnosis process and results of a single tower double span double cable planes cable stayed bridge were presented, and a high precision evaluation model covering random errors for damage diagnosis of cable stayed bridges was obtained.