To monitor the quality change of cucumber during storage, a feature extraction method of 3D fluorescence information of storage room gas (feature excitation wavelength and feature emission wavelength) is proposed using multivariate statistical analysis coupled with wavelet packet energy during different cucumber storage dates. Firstly, these 3D fluorescence data were handled by removing Rayleigh scattering and polynomial Savitzky-Golar (SG) smoothing to remove the effects of scattering and noise signals. Secondly, the pre-processed 3D fluorescence data were handled by principal component analysis (PCA) to obtain the principal component matrix, and Wilks statistics were constructed by using each principal component variable, then the principal component corresponding to the minimum value (the 11th principal component, PC11) was selected. Then eight feature excitation wavelengths were extracted according to the combination coefficient of each original variable (excitation wavelength) of the principal component. Thirdly, the emission spectrum is divided by 10nm interval and gets 26 bands; the 3-scale based wavelet packet decomposition (WPD) was carried out for each band, and the wavelet packet energy of each band after decomposition was calculated. And then, according to the analysis results of 8 days test data, the band with the highest energy was selected as the primary feature emission band. Fourthly, partial least squares regression (PLS) was used to analyze the primary emission bands combined with the physicochemical indexes (hardness, chlorophyll content and weight loss rate) of cucumber, and seven feature emission wavelengths were selected according to the regression coefficient, which greatly simplified the calculation. At the same time, according to the hardness data of cucumber, the turning point of its trend change could be found; and according to the cucumber chlorophyll content data and its first derivative, the chlorophyll decline the most significant points could be also found; and then combined with the sensory analysis in the process of test, ultimately determine the quality of cucumber stored at the fifth day become bad rapidly. Therefore, the fifth storage day was chosen to monitor the reference date. Finally, Mahalanobis Distance (MD) between different storage days and monitoring reference dates was calculated using the extracted feature fluorescence information, and the MD monitoring model was constructed. The results show that the MD decreased gradually to 0 with the storage time approaching the monitoring reference date, which was consistent with the quality change process of cucumber during storage. Therefore, the above feature wavelength extraction method based on multivariate statistical analysis combined with wavelet packet energy and the MD monitoring model is expected to be an effective method for quality monitoring of cucumber during storage.