The invention discloses a method for recognizing an abnormal condition of bridge monitoring data based on fuzzy clustering. The method comprises the following steps: 1) taking the monitoring data under the normal running state of a bridge as a to-be-analyzed training sample set; 2) classifying the monitoring data according to different temperature monitoring conditions; 3) adopting a pauta criterion for primarily screening all the classifications and removing noise and isolated points; 4) endowing all the samples with different fuzzy membership values; 5) performing fuzzy clustering on the primarily screened classifications according to different fuzzy membership; 6) respectively treating the training samples under different temperatures, thereby acquiring class centers and class boundaries under different temperatures; 7) comparing the class centers and class boundaries, thereby judging the running condition of the bridge. According to the invention, the fuzzy clustering technique is adopted for analyzing and processing mass bridge monitoring data. The alarm threshold value at a measured point is corrected on the basis of a statistics principle, so that the alarm threshold value can be more suitable for the practical running state of the bridge.