Swift response to public complaints is an important measure to realize intelligent social governance and improve people’s satisfaction. It is particularly crucial to analyze public complaints accurately to match work order processing departments intelligently， and to realize swift response and efficient handling of public complaints. However， the vague description of complaints， confusion of categories and imbalance of proportion in public complaint data lead to difficulties in analyzing categories of complaints， thus reducing the efficiency and accuracy of intelligent order dispatching. To solve the above problems， a hierarchical multi-label classification model （HMCHotline） for complaints with encoder-decoder structure was proposed. Firstly， the fine-grained keyword prior knowledge in complaint domain was introduced into the text encoder to suppress noise interference， and the spatio-temporal information in complaints was fused to improve the discriminant ability of semantic features. Secondly， the label hierarchy was used to generate label embeddings with hierarchy-awareness and semantic-awareness， and a label decoder based on the Transformer model was constructed to decode labels using the semantic features from the complaints and label features. At the same time，the dynamic label table strategy was introduced based on the hierarchical dependency to limit the decoding range of labels for solving the problem of label inconsistency. Finally， the Softmax grouping strategy was used to divide the label categories with the similar size into the same group for Softmax operation， which alleviated the problem of low classification accuracy caused by the long-tailed distribution of labels. Experimental results on Hotline， RCV1 （Reuters Corpus Volume I） -v2 and WOS （Web Of Science） datasets show that compared with Hierarchy-aware label semantics Matching network （HiMatch）， the proposed model improves the Micro-F1 by 1.65， 2.06 and 0.43 percentage points respectively， proving the effectiveness of the proposed model.

To solve the problem that the Transformer-based super-resolution network cannot fully utilize the surrounding information， a Mixed Attention Transformer image super-resolution network （MAT） based on neighborhood attention was proposed. Firstly， a convolutional layer was used to extract shallow features， and a series of Residual Mixed Attention Group （RMAG） and a 3×3 convolutional layer were used for deep feature extraction. In this way， the neighborhood attention and channel attention methods were combined， making full use of the complementary advantages of the two methods， that was the ability to utilize global statistics and strong local fitting simultaneously. In addition， an overlapping cross-attention module was introduced to enhance the interaction between adjacent window features. Secondly， a global residual connection was added to fuse shallow features and deep features. Finally， with pixel shuffling method adopted， the reconstruction module was used to upsample the fused features. Experimental comparison results of MAT and multiple algorithms such as RCAN （Residual Channel Attention Network）-it on multiple datasets show that the Peak Signal-to-Noise Ratio （PSNR） of the proposed algorithm is significantly higher than the advanced methods by 0.3 to 1.0 dB. It can be seen that MAT improves the image restoration effect in image super-resolution tasks effectively.