As a key mode of transportation, bridges bear the high pressure of traffic flow. Many bridges have defects before reaching their designed service life. Bridge-defect recognition based on visible light uses grayscale defect images and regional edge gradient information, which have limitations in complex environments. The radiation and absorption of spectral band signals by objects are detected by hyperspectral imaging, and the signals are transformed into images and graphics. The physical properties of the measured object are analyzed based on the position and intensity of the absorption peak. In this study, a method based on hyperspectral vision is proposed to identify exposed reinforcement bar defects in bridge concrete. Based on the spectral lines and spatial features of hyperspectral images of exposed reinforcement defects in bridge concrete combined with processing——Smooth filtering multivariate scattering calibration (SG-MSC), feature space transformation——First derivative method (FD), and feature variable selection algorithm——Competitive adapative reweighted sampling (CARS), the original spectral curve data were transformed into feature space to extract the corresponding feature values and display the band. The dataset was constructed based on spectral curve feature vectors, and a support vector machine algorithm was used to establish a prediction model for identifying exposed reinforcement defects.Considering a cross-river bridge as an example, a hyperspectral visual testing system was used to identify actual exposed reinforcement bar defects of the bridge. By performing smooth feature space transformation and feature extraction on the original spectral data, the differences were amplified, reducing the dimensionality of the 254 band data to 23 band data and achieving a model prediction accuracy of 94.6%.Hyperspectral vision has higher dimensional information than visible-light vision. Hence, the proposed model can effectively characterize material properties, is feasible, and has broad application prospects.