Aiming at monitoring the batch process with complex nonlinear characteristic, a multiway kernel entropy component analysis (MKECA) method based on the angle structure statistic is proposed. In this method, the process data is firstly preprocessed, and then the principal component matrices of the batch process data are extracted by KECA. Research shows that KECA reveals angular structure relating to the Renyi entropy of the input space data set, and angular structure statistic is constructed using the principal component matrix structure. And then the control limits are calculated by the kernel density estimation algorithm. Finally， through the simulation of the penicillin fermentation and the actual production process of recombinant, the experiment results show that the proposed method effectively uses the structural information of the principal components compared to the traditional method of process monitoring. So error rate and false alarming rate are significantly lowered.