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A brief overview of high-dimensional space processing state of development, as well as it contains some of the issues, such as "dimension disaster" problem, the special high-dimensional space, high dimensional data intrinsic dimension and so on. At the same time commonly used linear dimension reduction methods, is mainly used principal component analysis theory and nonlinear dimensionality reduction methods, mainly locally linear embedding (LLE) principle of introduction. 2. Gives the high-dimensional data processing method in dimensionality reduction distillation process of terephthalic acid in an application example, and gives a linear dimensionality reduction method, principal component analysis in this distillation process in the processing and dimensionality reduction conclusions, as well as Nonlinear dimensionality reduction of LLE to reduce the dimension of the distillation process. 3. Comparison Good or bad of three methods to predict the dependent variable, that is the BP neural network, principal component analysis with BP neural network and LLE with BP neural network. If the Water content is dependent variable, LLE with BP is best, principal component analysis with BP is second and BP is worst, while the acetic acid content is dependent variable, BP is best, principal component analysis with BP is second andLLE with BP is worst. Key words: high-dimensional data; dimensionality reduction; principal component analysis; LLE, BP neural network