With the application of artificial intelligence technology in the judicial field， charge prediction based on case description has become an important research content. It aims at predicting the charges according to the case description. The terms of case contents are professional， and the description is concise and rigorous. However， the existing methods often rely on text features， but ignore the difference of relevant elements and lack effective utilization of elements of action words in diverse cases. To solve the above problems， a multi-task learning model of charge prediction based on action words was proposed. Firstly， the spans of action words were generated by boundary identifier， and then the core contents of the case were extracted. Secondly， the subordinate charge was predicted by constructing the structure features of action words. Finally， identification of action words and charge prediction were uniformly modeled， which enhanced the generalization of the model by sharing parameters. A multi-task dataset with action word identification and charge prediction was constructed for model verification. The experimental results show that the proposed model achieves the F value of 83.27% for action word identification task， and the F value of 84.29% for charge prediction task； compared with BERT-CNN， the F value respectively increases by 0.57% and 2.61%， which verifies the advantage of the proposed model in identification of action words and charge prediction.

Aiming at the problem of poor recognition of sentencing circumstances in adjudication documents caused by the lack of labeled data， low quality of labeling and existence of strong logicality in judicial field， a sentencing circumstance recognition model based on abductive learning named ABL-CON （ABductive Learning in CONfidence） was proposed. Firstly， combining with neural network and domain logic inference， through the semi-supervised method， a confidence learning method was used to characterize the confidence of circumstance recognition. Then， the illogical error circumstances generated by neural network of the unlabeled data were corrected， and the recognition model was retrained to improve the recognition accuracy. Experimental results on the self-constructed judicial dataset show that the ABL-CON model using 50% labeled data and 50% unlabeled data achieves 90.35% and 90.58% in Macro_F1 and Micro_F1， respectively， which is better than BERT （Bidirectional Encoder Representations from Transformers） and SS-ABL （Semi-Supervised ABductive Learning） under the same conditions， and also surpasses the BERT model using 100% labeled data. The ABL-CON model can effectively improve the logical rationality of labels as well as the recognition ability of labels by correcting illogical labels through logical abductive correctness.

Relation extraction aims to extract the semantic relationships between entities from the text. As the upper-level task of relation extraction， entity recognition will generate errors and spread them to relation extraction， resulting in cascading errors. Compared with entities， entity boundaries have small granularity and ambiguity， making them easier to recognize. Therefore， a relationship extraction method based on entity boundary combination was proposed to realize relation extraction by skipping the entity and combining the entity boundaries in pairs. Since the boundary performance is higher than the entity performance， the problem of error propagation was alleviated； in addition， the performance was further improved by adding the type features and location features of entities through the feature combination method， which reduced the impact caused by error propagation. Experimental results on ACE 2005 English dataset show that the proposed method outperforms the table-sequence encoders method by 8.61 percentage points on Macro average F1-score.

Flow entries are forwarding rules generated by controllers and guide switches to process data packets in Software Defined Network （SDN）. Every flow entry is stored in the memory of switches and has timeout， which affects the bandwidth cost in SDN control channel， the memory consumption in switches， and the system’s resource management and performance. As most of the existing SDN performance optimization schemes only have single objective， and do not consider the impact of the types and time of the flow entry timeouts， a multi-objective optimization scheme was proposed based on the dynamic mixed timeouts of flow entries to simultaneously optimize the three objects： the detection of elephant flows， the memory consumption of flow entries in switches， and the control channel bandwidth occupation. In the dynamic mixed timeout， hard-timeout and idle-timeout， two timeout methods of flow entries were combined， and the timeout type and time of flow entries were adjusted in a two-dimensional dynamic way. The NSGA-Ⅱ algorithm was used to solve the proposed optimization problem and to evaluate the impact of different timeout methods and timeout time on the three optimization objectives. The solution set of specific timeouts was combined with the solution set of Bayesian multi-objective optimization algorithm to improve the quality of the solution set. The results show that the proposed scheme can provide a higher detection accuracy， a lower bandwidth occupation， and a smaller switch memory consumption. It significantly improves the overall performance of SDNs.