In the task of Temporal Knowledge Graph Question Answering （TKGQA）， it is a challenge for models to capture and utilize the implicit temporal information in the questions to enhance the complex reasoning ability of the models. To address this problem， a Graph Attention mechanism-integrated Complex Temporal knowledge graph Reasoning question answering （GACTR） model was proposed. The proposed model was pretrained on a temporal Knowledge Base （KB） in the form of quadruples， and a Graph Attention neTwork （GAT） was introduced to effectively capture implicit temporal information in the question. The relationship representation trained by Robustly optimized Bidirectional Encoder Representations from Transformers pretraining approach （RoBERTa） was integrated to enhance the temporal relationship representation of the question. This representation was combined with the pretrained Temporal Knowledge Graph （TKG） embedding， and the final prediction result was the entity or timestamp with the highest score. On the largest benchmark dataset CRONQUESTIONS， compared to the baseline models， Knowledge Graph Question Answering on CRONQUESTIONS（CRONKGQA）， the GACTR model achieved improvements of 34.6 and 13.2 percentage points in handling complex question and time answer types， respectively； compared to the Temporal Question Reasoning （TempoQR） model， the improvements were 8.3 and 2.8 percentage points， respectively.

The flood of fake job advertisements will not only damage the legitimate rights and interests of job seekers but also disrupt the normal employment order， which results in a poor user experience for job seekers. To effectively detect fake job advertisements， an SSC （Semi-Supervised fake job advertisements detection model based on Consistency training） was proposed. Firstly， the consistency regularization term was applied on all the data to improve the performance of the model. Then， supervised loss and unsupervised loss were integrated through joint training to obtain the semi-supervised loss. Finally， the semi-supervised loss was used to optimize the model. Experimental results on two real datasets EMSCAD （EMployment SCam Aegean Dataset） and IMDB （Internet Movie DataBase） show that SSC achieves the best detection performance when the labeled data are only 20， and the accuracy is increased by 2.2 and 2.8 percentage points compared with the existing advanced semi-supervised learning model UDA （Unsupervised Data Augmentation）， and is increased by 3.4 and 11.7 percentage points compared with the deep learning model BERT （Bidirectional Encoder Representations from Transformers）. At the same time， SSC has good scalability.

Concerning the problems of poor classification performance and high computational overhead of Capsule Network （CapsNet） on complex images with background noise information， an improved capsule network model based on attention mechanism and weight sharing was proposed， called Shared Transformation Matrix CapsNet （STM-CapsNet）. The proposed model mainly includes the following improvement. 1） An attention module was introduced into the feature extraction layer of CapsNet， which enabled low-level capsules to focus on entity features related to the classification task. 2） Low-level capsules with close spatial positions were divided into several groups， and each group of low-level capsules was mapped to high-level capsules by sharing transformation matrices， which reduced computational overhead and improved model robustness. 3） The L₂ regularization term was added to margin loss and reconstruction loss to prevent model overfitting. Experimental results on three complex image datasets including CIFAR10， SVHN （Street View House Number） and FashionMNIST show that， the above improvements are effective in enhacing the model performance； when the number of iterations is 3， and the number of shared transformation matrices is 5， the average accuracies of STM-CapsNet are 85.26%， 93.17% and 94.96% respectively， the average parameter amount is 8.29 MB， verifying that STM-CapsNet has better performance compared with the baseline models.

The root cause of software security lies in the source code developed by software developers， but with the continues increasing size and complexity of software， it is costly and difficult to perform vulnerability detection only manually， while the existing code analysis tools have high false positive rate and false negative rate. Therefore， an automatic vulnerability detection method based on Relational Graph Convolution Network （RGCN） was proposed to further improve the accuracy of vulnerability detection. Firstly， the program source code was transformed into CPG containing syntax and semantic information. Then， representation learning was performed to the graph structure by RGCN. Finally， a neural network model was trained to predict the vulnerabilities in the program source code. To verify the effectiveness of the proposed method， an experimental validation was conducted on the real-world software vulnerability samples， and the results show that the recall and F1-measure of vulnerability detection results of the proposed method reach 80.27% and 63.78% respectively. Compared with Flawfinder， VulDeepecker and similar method based on Graph Convolution Network （GCN）， the proposed method has the F1-measure increased by 182%， 12% and 55% respectively. It can be seen that the proposed method can effectively improve the vulnerability detection capability.

Every Guaranteed Time Slot (GTS) mechanism in IEEE 802.15.4 standard can be allocated multiple slots to guarantee the real-time data transmission. For the lack of analyzing multi-slot GTS performances under contention free period, the network calculus, was used to analyze the service curves of delay bound and throughput, and the connection with energy consumption.The IEEE802.15.4 sensor node model which has proposed by Jurcik and Koubaa has been improved, and modeling and simulation were done to study how GTS parameters influenced the network performances (delay, throughput and energy consumption). The simulation results indicate that according to high or low burst data rate, there is an optimized allocation of GTS parameters to meet the real-time data transmission.