The “siege” of construction waste has become the main problem of urban environmental pollution at this stage, severely restricting the sustainable development of the urban ecological environment. A good classification of construction waste is of great significance to protecting urban water resources, improving the utilization rate of urban land and improving residents’ quality of life. In this paper, the GaiaSky-mini 2 push-broom airborne specular imager (400~1 000 nm) is mounted on the DJ MATRICE M600Pro UAV, and a clean and windless test environment is selected to collect hyperspectral remote sensing images of the study area in real-time. The hyperspectral remote sensing images of the study area were preprocessed by geometric correction, image cropping and radiometric correction; The objects in the study were divided into two categories: background objects, including reed, wormwood, water, shadow, bare soil and asphalt road, and construction waste including white plastic, dust cloth, foundation residue and rubble sand. Based on pixel points, select the regions of interest (ROI) of various features as training samples, extract the spectral information of six background features and four types of construction waste in the study area, and make spectral curves based on different spectral feature differences between features. Select feature bands, calculate statistics through bands and select reasonable thresholds, use decision tree classification to separate background features and identify and extract construction waste in the study area. Target different background features and construction waste types were selected to verify the sample points and evaluate the accuracy of the separation results of background features and the identification results of construction waste.The results show that the overall recognition and classification accuracy of background features and construction waste is 85.91%, and the Kappa coefficient is 0.845. According to the established decision tree for the separation of background features, the classification effect of six background features is good, among which the producer accuracy of reed, asphalt road and bare soil is 95%, and the overall separation of background features is good. According to the established construction waste identification decision tree, the producer accuracy of dust cloth and rubble sand is 95%, and the producer accuracy of white plastic and foundation residue is 90%, which can accurately extract construction waste in the study area. This study shows that decision tree classification is realized in the unmanned aerial vehicle (UAV) hyperspectral remote sensing image recognition and extraction of the construction waste has good classification accuracy. Moreover, to verify the unscrewed aerial vehicle (UAV) hyperspectral remote sensing in the field of construction waste classification to extract the scientific nature and feasibility of construction waste classification recognition for future work has a specific practical significance.