Chlorpyrifos is one of the most widely used organophosphorus pesticides (OPs) in agricultural production. However, pesticide residues caused by excessive OPs pose a serious threat to the natural environment and human life and health. Therefore, it is of great significance to develop a rapid, accurate, convenient, and economic method for directly detecting OPs residues in agricultural products. Four groups of chlorpyrifos pesticide solutions with different volume concentrations (1:200, 1:500, 1:800, 1:1 000) were prepared, the control group was treated with pure water.Cabbage leaves were soaked in chlorpyrifos pesticide solutionsfor 3 minutes, 30 leaf samples were collected from each group, and 150 samples were collected from 5 groups. The spectrum information of Chlorpyrifos in cabbage leaves was obtained by visible near-infrared spectroscopy (NIR), and the qualitative analysis of chlorpyrifos pesticide residues in cabbage leaves was carried out. In modeling, 24 samples in each group, 120 samples of 5 groups are taken as modeling training set, 6 samples in each group and 30 samples of 5 groups are taken as prediction set. The near-infrared spectrum analysis will be interfered with by factors such as uneven leaf surface, more wrinkles and different color of cabbage leaves, which makes the establishment of prediction model more difficult. In this paper, an all-band average grouping integration preprocessing method is proposed. The spectral bands are averagely divided into n groups, and then each group of data is integrated as new data for neural network modeling. The experimental results show that the all-band average grouping integration preprocessing method proposed in this paper has the best modeling effect using the spectral reflectance first derivative (FD) when the group number is n=25. The modeling set recognition accuracy is 97.50%, and the prediction set recognition accuracy is 96.67%. The modeling effect is better than the commonly used spectral sensitive and characteristic band modeling method (with modeling set recognition accuracy 91.67%). The all-band average grouping integration preprocessing method can retain more characteristic bands of spectral data and reduce the dimension of spectral data, reducing the impact of single spectral data noise on the modeling effect. Selecting the appropriate grouping number could achieve good modeling and prediction effect. The results of this study can provide a reference for the application of visible near-infrared spectroscopy in the detection of chlorpyrifos pesticide residues.