Wool products are popular because of their softness and warmth. The content of wool is an important indicator of the quality of wool products. However, the quality of wool products in the market varies. In addition, traditional testing methods are destructive, and the results might be subjective, which can no longer meet the need to evaluate the quality of the target wool products quickly. NIR spectroscopy is a rapid measurement method that does not require the destruction of sample structure and can be embedded with machine learning models. Because of this, this paper proposes a rapid qualitative wool content evaluation method via fusing NIR spectroscopy and attention-based U-Net++. In terms of data preparation, this paper employs a handheld portable spectrometer to collect spectral data of wool product samples with a wavelength range of 908.1 to 1 676.2 nm. The original samples are graded according to their contents. The experiments collected spectral datasets of the same sample at 5 heights of 5, 6, 8, 9 and 19 mm from the spectrometer, and abnormal samples were removed by Mahalanobis distance. 5 125 sets of spectral data were used for the final data modeling. Regarding model selection, the U-Net++ network provides an end-to-end way for feature extraction and classification with down-sampling, jump connections and up-sampling operations. However, due to alarge number of skip connections, it reuses low-level features, and the models might contain redundant parameters. This paper introduces an attention-gating module which can extract feature information more effectively and improve prediction accuracy. The spectral data corresponding to 90% of wool product samples is used for training and validation, and the rest spectral data is used for testing. The experimental results show that the prediction model based on the U-Net++ network obtains an accuracy of 93.59%, a recall of 93.53%, and a precision of 94.24% on the independent test set, all of which outperform traditional classification models. Meanwhile, the classification model proposed in this paper outperforms other U-Net series networks, such as U-Net and Attention U-Net, demonstrating the effectiveness of the skip connection and attention-gating modules. In this paper, the spectral analysis based on the Attention U-Net++ model and portable near-infrared spectrometer provides a practical and meaningful way for rapid, nondestructive inspection of wool content.