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The process of drum drying silk has complex characteristics of multivariability, strong coupling, and nonlinearity. Traditional Principal Component Analysis (PCA) method lacks strong nonlinear ability, and global modeling method is difficult to achieve accurate fault detection of the process. Therefore, this paper proposed a novel multi-block approach by integrating Autoencoder (AE) for feature extraction and PCA model. Initially, in order to capture local features, the process variables were divided into blocks according to the drying process principle of tobacco leaf. Secondly, autoencoder was used to extract the nonlinear features of each sub-block. Then, the corresponding PCA models are established for each sub-block, respectively. Lastly, the monitoring results of multiple subspaces were fused for decision-making by Bayesian inference. Two actual leaf silk drying cases were used for verification, and the results showed that the alarm rates of this method were as high as 91.67% and 98.21%. Compared to traditional PCA and AE-PCA detection methods, this algorithm could accurately reveal and characterize the overall operating status and local feature information of the drying process, improve the accuracy of anomaly detection in the drum leaf silk drying production process and achieve accurate alarm for quality anomalies, to ensure stable production of the drum leaf silk drying process. [ABSTRACT FROM AUTHOR]