Abstract: In order to improve the quality instability of instant rice during processing and shorten the development cycle of instant rice, a method to accurately predicted the quality of instant rice based on process parameters was adopted. Principal component analysis (PCA) was used to reduce the dimensionality and classify the appearance quality (L^*, a^*, b^*, W, porosity, specific volume), textural characteristics (hardness, viscosity, elasticity, chewiness, cohesiveness, resilience), and sensory evaluations (smell, color, integrity, viscosity, elasticity, hardness, total sensory score) of instant rice. Subsequently, using the reduced-dimensional data with process parameters as input variables and comprehensive score as output, a random forest prediction model was established. The accuracy of the regression prediction model and its fit to the data were evaluated using mean square error (MSE), mean absolute error (MAE), and the coefficient of determination (R²). The results showed that after PCA dimensionality reduction, the quality of instant rice could be classified into four categories: excellent (1-2), good (0-1), medium (-1 to 0) and poor (-2 to -1). When the number of decision trees in the RF algorithm was 800 and the maximum depth was 5, the prediction error of the prediction model had the smallest prediction error and highest accuracy. The prediction accuracy of the model obtained by PCA dimensionality reduction was better than that of the linear discriminant analysis (LDA) method. When compared with PCA-BP, PCA-PLS and PCA-ELM prediction models, the PCA-RF prediction model had the smallest MAE and MSE, with an R² of 0.898, which was higher than that of the other three prediction models. The established PCA-RF model exhibited good prediction performance, small prediction errors, high prediction accuracy, and strong learning and generalization abilities, providing a reference for quality control in the industrial production of instant rice.