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.

In view of the high cost as well as the limitation of retrieval function of the existing searchable encryption schemes based on blockchain to realize result verification and fair payment， a multi-keyword ranked search scheme supporting verification and fair payment was proposed based on blockchain. In the proposed scheme， the Cloud Service Provider （CSP） was used to store the encrypted index tree and perform search operations， and a lookup table including verification certificates was constructed to assist the smart contract to complete the verification of retrieval results and fair payment， which reduced the complexity of smart contract execution and saved time as well as expensive cost. In addition， the index of balanced binary tree structure was constructed by combining vector space model and Term Frequency-Inverse Document Frequency （TF-IDF）， and the index and query vectors were encrypted by using secure K -nearest neighbor， which realized the multi-keyword ranked search supporting dynamic update. Security and performance analysis show that the proposed scheme is secure and feasible in the blockchain environment and under the known ciphertext model. Simulation results show that the proposed scheme can achieve result verification and fair payment with acceptable cost.

In order to further improve the speed and precision of multi-scale object detection， and to solve the situations such as miss detection， wrong detection and repeated detection caused by small object detection， an object detection algorithm based on improved You Only Look Once v3 （YOLOv3） was proposed to realize automatic detection of multi-scale object. Firstly， the network structure was improved in the feature extraction network， and the attention mechanism was introduced into the spatial dimensions of residual module to pay attention to small objects. Then， Dense Convulutional Network （DenseNet） was used to fully integrate shallow information of the network， and the depthwise separable convolution was used to replace the normal convolution of the backbone network， thereby reducing the number of model parameters and improving the detection speed. In the feature fusion network， the bidirectional fusion of the shallow and deep features was realized through the bidirectional feature pyramid structure， and the 3-scale prediction was changed to 4-scale prediction， which improved the learning ability of multi-scale features. In terms of loss function， Generalized Intersection over Union （GIoU） was selected as the loss function， so that the precision of identifying objects was increased， and the object miss rate was reduced. Experimental results show that on Pascal VOC datasets， the mean Average Precision （mAP） of the improved YOLOv3 algorithm is as high as 83.26%， which is 5.89 percentage points higher than that of the original YOLOv3 algorithm， and the detection speed of the improved algorithm reaches 22.0 frame/s. Compared with the original YOLOv3 algorithm on Common Objects in COntext （COCO） dataset， the improved algorithm has the mAP improved by 3.28 percentage points. At the same time， in multi-scale object detection， the mAP of the algorithm has been improved， which verifies the effectiveness of the object detection algorithm based on the improved YOLOv3.

For its sensitive dependence with the Initial value, chaos can be applied to the pattern recognition of the ones with extremely small difference. An algorithm based on chaotic neural network was proposed and used for face recognition. For introducing chaotic noise, the network obtains a better anti-jamming. It can avoid being affected by the factors such as illumination and gesture. And many complex feature extractions can be avoided. Experimental results based on ORL face database show that the precision of the chaotic neural network algorithm is higher and the iteration steps are fewer and the speed of convergence is quicker. Chaotic neural network used for face recognition is effective and it can enhance recognition rate.