Lettuce is one of the vegetables that people often eat, and the storage time of lettuce is an important factor affecting the freshness of lettuce. Therefore, it is necessary to develop a simple, fast, and non-destructive method to identify the storage time of lettuce. Near-infrared spectroscopy (NIR) can quickly and accurately detect the near-infrared spectrum of lettuce to realize the non-destructive identification of lettuce storage time. However, noise and redundant signals are in the NIR spectral data collected by the near-infrared spectrometer. To eliminate the noise information of the spectrum and extract the feature information, a novel method was proposed to identify the storage time of lettuce based on NIR spectroscopy and fuzzy uncorrelated QR analysis (FUQRA). Firstly, principal component analysis (PCA) was used to reduce the dimension of the original spectral data from 1557 dimensions to 22 dimensions. Secondly, after the feature vectors are obtained by fuzzy uncorrelated discriminant transformation (FUDT), the discriminant vector matrix is established by using the feature vectors, and the final discriminant vector matrix is obtained by QR decomposition. Finally, the k-nearest neighbor algorithm was utilized for classification. 60 fresh lettuce samples were selected as the research object. Firstly, the NIR spectral data of lettuce samples were collected by Antaris Ⅱ near-infrared spectrometer and detected once every 12 hours. Secondly, multivariate scatter correction (MSC) was used to reduce the noise signal in the NIR spectra. To verify the effectiveness of the proposed method, the experimental results were compared by four classification models: principal component analysis (PCA) combined with a K-nearest neighbor (KNN) algorithm, PCA and fuzzy linear discriminant analysis (FLDA) combined with KNN algorithm, PCA and fuzzy uncorrelated discriminant transformation (FUDT) combined with KNN algorithm and PCA and FUQRA combined with KNN algorithm. The classification accuracies produced by different values of the weight indexm were studied, and the most appropriate parameters were selected: m=2, K=3. Under this condition, the classification accuracies of the four algorithms were 43.33%, 96.67%, 96.67%, and 98.33%, respectively. It can be seen that compared with the other three algorithms, FUQRA can better realize the identification of lettuce storage time.