Barnyard grass is a typical weed in paddy fields that can severely affect the growth and development of rice, ultimately leading to reduced yields. Its appearance is very similar to that of rice, making it difficult to distinguish and posing significant challenges for management. Ideal conditions for barnyard grass identification are achievable in controlled indoor settings but are difficult to replicate in practical applications. For this reason, identifying and mapping barnyard grass in complex paddy field environments holds significant research value and importance. First, hyperspectral images of the paddy fields were captured using UAVs. After image stitching, rectification, and SG (Savitzky-Golay) convolution filtering, a sequential projection algorithm (SPA) was employed to extract sensitive bands for distinguishing rice from barnyard grass. Modeling was performed across the entire spectral range and selected feature bands, employing support vector machines (SVM), random forests (RF), one-dimensional convolutional neural networks (1DCNN), and three-dimensional convolutional neural networks (3DCNN). The results indicated that SPA-3DCNN achieved the best recognition performance for rice (0.942 0) and barnyard grass (0.893 6). The seven feature bands selected by SPA (482.523 4, 546.541 5, 675.080 6, 709.138 2, 762.043 1, 922.015 7, and 944.637 1 nm) were valuable for distinguishing barnyard grass from rice. Subsequently, the model was applied to the entire hyperspectral dataset to generate spatial distribution and density maps of barnyard grass. This study successfully explored the feasibility of UAV-based hyperspectral identification of barnyard grass in complex paddy field environments, providing strong data support for the management and prevention of barnyard grass.