Due to the strong contamination， high noise level， excessive exposure， and other problems in flame images from domestic Municipal Solid Waste Incineration （MSWI） processes， traditional target recognition methods are difficult to apply to them. Therefore， an MSWI incineration image classification framework — SAswin with Multilayer Preprocessing Network （SAswin-MPNet） was proposed. Firstly， a Transformer-based Hybrid Attention Super-Resolution Transformer （HASRT） module was designed to perform super-resolution reconstruction to the images. Secondly， a Practical Exposure Correction （PEC） module was introduced to correct the exposure of high-resolution MSWI images， thereby obtaining multilayer preprocessed data. Additionally， a validation algorithm was designed to compare and test the preprocessed images and the originals， and the images meeting a validation threshold were used to replace the originals， thereby obtaining a multilayer preprocessed dataset. Finally， an SAswin classification network was constructed to recognize incineration states. Experimental results based on actual operational data from an MSWI power plant comparing with ResNet-34， ResNet-50， ConvNeXt， ViT （Vision Transformer）， Swin-T （Swin-Tiny）， and EVA-02 （Enhanced Visual Assistant-02） show that SAswin-MPNet achieves the optimal MSWI image incineration state recognition accuracy and F1-score.