Infrared spectroscopy is a technology used to identify the chemical composition of substances based on molecular vibration and quantum-jump theory. Due to the unique absorbance of different functional groups, the spectral data related to the absorbance and the wavelength (or wavenumber) can be obtained when the infrared beam irradiates the molecular. However, the spectral data from experiments always have high dimensions and overlap, making it difficult to process the data. Thus, this paper proposed an improved Gustafson-Kessel possibilistic c-means clustering (GKIPCM), introducing the Mahalanobis distance from GK clustering and the iterative equations of fuzzy membership values and cluster centers from improved possibilistic c-means clustering (IPCM). GKIPCM makes the data adapt to different mathematical distance measures and avoids identical cluster centers. Furthermore, GKIPCM has higher classification accuracy, which is less sensitive to parameters. In the experiments, four groups of washed Chinese cabbage were the objects of spectral analysis and different concentrations of lambda-cyhalothrin pesticide were sprayed on the Chinese cabbages. Spectral data of Chinese cabbages were collected with Agilent Cary 630 FTIR spectrometer. Firstly, multiplicative scatter correction (MSC) was applied to reduce the noise and eliminate data offset when pre-processing the data. Secondly, principal component analysis (PCA) was utilized to reduce dimensions due to the wide wavenumber range (4 300~590 cm^-1) and the high data dimensions (971). After conducting PAC, the dimensionality of data was reduced to 23, and the total contribution of 23 principal components reached 99.60%. Nonetheless, the feature information was still mixed. So the linear discriminant analysis (LDA) was used to extract features of the spectral data, and the LDA algorithm reduced the dimensionality of the spectral data to 3. Finally, the fuzzy c-means clustering (FCM) was employed to obtain the optimal initial cluster centers. Then, the GKIPCM algorithm was applied to cluster four different groups of spectral data. Comparisons were made among the clustering results of GKIPCM, GK and IPCM. The running time and accuracy of GKIPCM were 0.218 8 seconds and 97.22%, and those of GK and IPCM were 0.093 8 seconds and 63.89%, 0.062 5 seconds and 91.67%, respectively. According to the results of the experiments, the GKIPCM algorithm finished the qualitative analysis of different concentrations of pesticide residues by analyzing the spectral data.