Abstract: Processing pressure is an important parameter during the production of Qingzhuan tea, and also for the identification of tea quality. Taking 100g Qingzhuan tea as the research object, this study aims to optimize the processing pressure, and then establish a quantitative model of key components, including pressure, quality and contents of Qingzhuan tea, using the near infrared spectroscopy. The processing pressure was set as 3, 6, 12, 18 and 24 MPa in the test. The sensory evaluation and correlation methods were used to analyze the relationship between the optimal pressure, the quality and the contents of Qingzhuan tea. Standard normal variate (SNV), multiple scatter correction (MSC), first derivative (FD) and second derivative (SD) and their combined methods were used to denoise the original raw spectrum during the preprocessing of data. Then, the backward partial least squares algorithm was used to select the characteristic spectral intervals, while the principal component analysis method was used to analyze them. Finally, the principal components were input into the jump connection nets structure artificial neural network (ANN) of three kinds of transfer functions, as linear 0,1 functions, logistic functions and tanh functions, respectively, to establish a quantitative analysis model. The results showed that 1) the optimum pressure was 18 MPa, while the content of gallocatechin gallate (GCG) was closely related to the pressure and the quality of Qingzhuan tea (P<0.05); 2) the optimum pretreatment method was MSC+FD method; 3) the characteristic spectral intervals were 9 734.9-10 000, 8 924.9-9191.1 cm-1, 5 368.9-5 638.8, 7 011.9-7 281.9, 6 190.4-6 460.4, 4 821.2-5 091.2, 9 194.9-9 461.1 cm-1, 7 559.6-7 829.6, 5 916.5-6 186.5 cm-1; 4) the cumulative contribution rate of the first three principal components was 97.82%; 5) the GCG artificial neural network model that established by tanh transfer function indicated the best results (Rp2=0.980, RMSEP = 0.027), with better practical application effect (Rp2=0.948, RMSEP=0.041). The findings can provide a theoretical foundation to develop more types of Qingzhuan tea products, and to rapidly detect their quality in tea industry.