Aiming at the problems of complex entity overlap situations and difficulties in extracting multiple relational triples in natural language texts， a joint triple extraction model combining pointer network and relational embedding was proposed. Firstly， the BERT （Bidirectional Encoder Representations from Transformers） pre-training model was used to encode and represent the input sentence. Secondly， the head and tail pointer labeling was used to extract all subjects in the sentence， and the attention mechanism guided by subjects and relations was used to distinguish the importance of different relation labels to each word， so that the relation label information was added to the sentence embedding. Finally， for the subjects and each relation， the corresponding object was extracted by using the pointer labeling and cascade structure， and the relational triples were generated. Extensive experiments were conducted on two datasets， New York Times （NYT） and Web Natural Language Generation （WebNLG）， and the results show that the proposed model has better overall performance than the current best Novel Cascade Binary Tagging Framework （CasRel） model by 1.9 and 0.7 percentage points respectively； compared with the Extract-Then-Label method with Span-based scheme （ETL-Span） model， the performance improvements of the proposed model are more than 6.0% and more than 3.7% in the comparison experiments with 1 to 5 triples， respectively. Especially in complex sentences with more than 5 triples， the proposed model has the F1 score improved by 8.5 and 1.3 percentage points respectively. And stable extraction ability of this model is maintained while capturing more entity pairs， which further verifies the effectiveness of this model in triple overlap problem.

Under the constrains of very short message content on social media platforms， a large number of empty forwards in the transmission structure， and the mismatch between user roles and contents， a rumor detection model based on user attribute information and message content in the propagation network， namely GMB_GMU， was proposed. Firstly， user propagation network was constructed with user attributes as nodes and propagation chains as edges， and Graph Attention neTwork （GAT） was introduced to obtain an enhanced representation of user attributes； meanwhile， based on this user propagation network， the structural representation of users was obtained by using node2vec， and it was enhanced by using mutual attention mechanism. In addition， BERT （Bidirectional Encoder Representations from Transformers） was introduced to establish the source post content representation of the source post. Finally， to obtain the final message representation， Gated Multimodal Unit （GMU） was used to integrate the user attribute representation， structural representation and source post content representation. Experimental results show that the GMB_GMU model achieves an accuracy of 0.952 on publicly available Weibo data and can effectively identify rumor events， which is significantly better than the propagation algorithms based on Recurrent Neural Network （RNN） and other neural network benchmark models.

The multi-agent collaborative pursuit problem is a typical problem in the multi-agent coordination and collaboration research. Aiming at the pursuit problem of single escaper with learning ability, a multi-agent collaborative pursuit algorithm based on game theory and Q-learning was proposed. Firstly, a cooperative pursuit team was established and a game model of cooperative pursuit was built. Secondly, through the learning of the escaper’s strategy choices, the trajectory of the escaper’s limited Step-T cumulative reward was established, and the trajectory was adjusted to the pursuer’s strategy set. Finally, the Nash equilibrium solution was obtained by solving the cooperative pursuit game, and the equilibrium strategy was executed by each agent to complete the pursuit task. At the same time, in order to solve the problem that there may be multiple equilibrium solutions, the virtual action behavior selection algorithm was added to select the optimal equilibrium strategy. C# simulation experiments show that, the proposed algorithm can effectively solve the pursuit problem of single escaper with learning ability in the obstacle environment, and the comparative analysis of experimental data shows that the pursuit efficiency of the algorithm under the same conditions is better than that of pure game or pure learning.

For the complex tasks with time constraints, which can dynamically be added to environment, a task allocation model based on game theory was established, and a task allocation method was proposed, which made Computer Generated Actor (CGA) be able to choose its actions according to the local information owned by itself, and ensured that CGA learned a strict pure strategy Nash equlilibrium quickly by using fictitious play method on behavior coordination. The simulation results show that this method is reasonable, and it can effectively solve the dynamic task allocation problem.

A hierarchical multi-phase C-V method based on narrow band was proposed. Several stages are involved in the proposed method, and each stage needs just one level set. Besides, in every segmentation stage only the points of narrow band need to be dealt with. Therefore, the computation work has been greatly reduced compared with the multi-phase C-V method. Experimental results of MRI (Magnetic Resonance Imaging) image show that the proposed method is efficient and stable. The segmentation effect and speed is improved greatly when the image has complex structure.