The invention discloses a neural network prediction method based on principal component analysis, and the method comprises the following steps: 1, collecting process data and quality data, and carrying out the processing of the data through the principal component analysis; and step 2, establishing a neural network model by using the data obtained in the step 1, and performing prediction. According to the method, firstly, process variables and quality variables generated in the chemical process are collected, a principal component analysis method is used for preprocessing data, the data dimension is reduced, redundancy is avoided, the processed data are input into a prediction model of the radial basis function neural network, corresponding parameters are solved and optimized, and the model prediction accuracy reaches a preset value. Different from a traditional prediction method, the method combines a principal component analysis method and a radial basis function neural network model, reduces the complexity of modeling, and improves the precision of the model.