Bridge crack detection has great significance for bridge condition monitoring. Distributed fiber optic sensors are widely used to detect bridge cracks. The state quo approach is based on Brillouin time domain analysis. Though being capable of measuring strain data across the surface of the structure, it has its own flaw—the strain anomaly at the crack damage is “submerged” and “confused” by the noise due to the lower signal-to-noise ratio of the measured strain data. To this end, we propose a classification detection method based on one-dimensional stacked convolution autoencoder, which are endowed with strong noise robustness and high resolution of auto extraction features. The proposed method consists of three steps. First, structural surface strain data is acquired by arranging fiber optic sensors, the fiber strain data preprocessed and the strain subsequence divided. Second, the characteristics of the strain subsequence are automatically extracted using a one-dimensional stacked convolution autoencoder. Finally, the extracted strain subsequence features are classified by a Softmax classifier into two categories—cracks or non-cracks. The method can effectively detect micro cracks and has high detection accuracy. Moreover, by experimental contrast we claim that the feature discriminability extracted by this method is better than that of convolutional neural network and stacking autoencoder.