In this article, a hierarchical multi-block stacked performance-relevant denoising auto-encoder (HMSPDAE) is proposed to evaluate the process operating performance for plant-wide industrial processes with multiple sub-processes, low data difference among different operating performances, and strong noise interference. First, the whole process is divided into a hierarchical structure according to the process characteristics. Then, a method of stacked performance-relevant denoising auto-encoder is proposed to extract the performance-relevant deep features from the process data which are used to realize the operating performance assessment of each subprocess as well as the whole process. In further, a HMSPDAE-based whole-process evaluation model is formulated. The proposed method can effectively reduce the model complexity and enhance the interpretability of the model. Finally, simulation experiments are conducted in the wet metallurgical process. The results show that the assessment accuracy of HMSPDAE reaches 99. 5% and 99. 38% in two different experiments, which are both better than other methods.