The technology of near infrared spectroscopy that has unique advantage in fault detection in industrial processes is an accurateand effective method. Combining the mutual information entropy and the traditional principal component analysis, a new method for extracting the near infrared spectral feature information was first developed. The operating states of the industrial process was described by the constructed process pattern.Near infrared spectroscopy data were used to obtain the process pattern of industrial systems from the vibration information of hydrogen groups in organic molecules in this paper. An effective method to improve accuracy of fault detection in industrial processes was explored from the microscopic molecular level. Combined with Bayesian statistical learning method, an industrial processes fault detection technique based on near infrared spectroscopy data was proposed. Firstly, for the characteristics of rich information, wide spectrum band and weak characteristic, first-order derivative preprocessing of near infrared spectroscopic absorbance data under different operating states of industrial process was applied. Principal component analysis(PCA) was used to compress the amount of spectral data, expand the differences in spectral feature information under different operating states, and extract the internal feature information of the spectrum. Then, mutual information entropy(MIE) was used as correlation measure function of spectral feature information, and the minimum redundancy maximum relevance algorithm was used to further reduce the redundancy between the spectral feature information and maximize the relevance between the spectral and class.It made up for the deficiency of unsupervised feature wavelength selection of PCA. Therefore, a process pattern construction method based on PCA-MIE was proposed. The obtained process pattern subset was more compact and more expressive. Furthermore, Bayesian statistical learning method was applied to make decisions based on posterior probability of the constructed process pattern subset to identify the normal and accident state of the production process. Because the process pattern subset combines the advantages of PCA in density variance reduction and the feature information selection method of mutual information entropy correlation measure, it contains more essential information and inherent laws of near infrared spectroscopy, which can better describe the operating states of the industrial process. Next, The test accuracy (TA) was set as the evaluation criteria to evaluate the performance of the fault detection method. Finally, the data of crude oil desalination and dehydration process provided by the chemical plant was used to verify the effectiveness of the proposed method. Compared with the performance of traditional near infrared spectral feature information selection methods PCA and MIE, the results showed that the process pattern fault detection based on PCA-MIE outperforms the other two methods on almost all dimensions subsets. The highest accuracy rate is 94.6% when the feature dimensions is 18, which proves the superiority of the proposed method.