The industrial process includes the dynamic and time-varying characteristics.The quality prediction method based on the tradition PLS model fails to learn and follow the process information on real time by using the fixed training data set, which often causes the problems of poor model effectiveness and accuracy on quality prediction performance.Thus, a new quality prediction method based on the adaptive block recursive partial least squares(Block_RPLS)is proposed to modify the construction and the parameter of PLS model online.The sliding window method is used to determine the updated data blocks and the structural characteristics of data blocks in this sliding window is analyzed by using the matrix similarity theory.At the same time, the local outlier factor(LOF) is introduced to detect the outlier data that has large discrete deviation in the sliding window, then, the time-varying information of the outlier data is adapted by the cross validation method to learn the PLS model parameters adaptively.Finally, the effectiveness of the proposed method is demonstrated by the numerical simulation and the experiment of the penicillin fermentation process.