Traditional chemical detection methods for analyzing pesticide residues in chinese cabbage are slow and destructive. In this study, a rapid, non-destructive method for identifying the types of pesticide residues in chinese cabbage samples was developed. First, the hyperspectral imaging system was used to analyze chinese cabbage samples exposed to one of four pesticides chlorpyrifos, dimethoate, methomyl and cypermethrin. The pesticide concentration ratios were 0.10, 1.00, 0.20 and 2.00 mg·kg^-1, respectively; and the data was compared to a pesticide-free sample. After 12 hours of natural degradation at room temperature, a hyperspectral imaging system corrected by a black and white plate was used to obtain 400~1 000 nm hyperspectral images of chinese cabbage samples, and the target area was selected by ENVI software. The specific regions of interest (ROI) in samples were further investigated, and the pre-processing by multiple scattering correction (MSC). Secondly, three algorithms such as competitive adaptive reweighting algorithm (CARS), principal component analysis (PCA), discrete wavelet transform (DWT) (based on db1, sym2, coif1, bior2.2, and rbio1.5 wave base functions) were then used to screen for dimensionality reduction from optimally pre-processed results. Finally, the screening results and the samples divided by the Kennard-Stone algorithm were adopted to construct three recognition models separately. Such as k-nearest-neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP) and convolutional neural network (CNN) were used to determine the best screening method for the dimension of pesticide residues and the optimal hyperspectral recognition model. Our results showed that the CNN, MLP, KNN, and SVM algorithms achieve the best overall accuracy (91.20%, 83.20%, 66.40%, and 90.40%, respectively), Kappa coefficient (0.89, 0.79, 0.58, and 0.88), and the prediction set time (86.01, 63.23, 20.02 and 14.03 ms) under the dimensionality reduction algorithm DWT, respectively; the wavelet basis function and the number of transform layers are coif1-2, coif1-4, bior2.2-2 and sym2-2. All three indicators are better than the modeling results based on CARS and PCA dimensionality reduction algorithms. It showed that the combination of discrete wavelet transform and convolutional neural network shortens the time of classification and identification and significantly improves the classification and identification accuracy, and improves the Hughes phenomenon, providing a new method for non-destructive and rapid detection and identification of chinese cabbage pesticide residues.