Emotion analysis can quickly and accurately dig out users’ emotional tendencies， and has a huge application market. Aiming at the complexity and diversity of the microblog language’s syntactic structures， a Syntax Graph Convolution Network （SGCN） model was proposed for fine-grained emotion classification of Chinese microblog. The proposed model has the characteristics of rich structural and semantic expression at the same time. In the model， a text graph was constructed on the basis of the dependency between words， and the correlation degree between words was quantified by Pointwise Mutual Information （PMI）. After that， the PMI was used as the weight of the corresponding edge to represent the structural information of the sentence. The semantic features fusing location information were taken as the initial features of nodes to increase the semantic features of nodes in the text graph. Experimental results on the microblog emotion classification dataset of Social Media Processing 2020 （SMP2020） show that for two sets of microblog data containing six categories of emotions： happiness， sadness， anger， fear， surprise， and emotionlessness， the average F1-score of the proposed model reaches 72.64% which is 2.75 and 3.87 percentage points higher than those of the BERT （Bidirectional Encoder Representations from Transformers） Graph Convolutional Network （BGCN） model and the Text Level Graph Neural Network （Text-Level-GNN） model， verifying that the proposed model can use the structural information of sentences more effectively to improve the classification performance than other deep learning models.

For deeply analyzing the factors that affect the prevalence of P2P botnets, the formation of a Peer-to-Peer (P2P) botnet was portrayed from dynamic perspective. Firstly, the differential equation model was formulated according to the formation of P2P botnets, which considered the effect of immunization on computer malware propagation. Furthermore, effective immune ratio of eliminating P2P botnets was calculated by analyzing steady condition of equilibrium in the model. Finally, the effective immune region was obtained and verified by deterministic simulation and stochastic simulation, respectively. The results show that the outbreak of P2P botnets can be effectively prevented by reasonable immune measures.

The wireless sensor network is event triggering network, which contains many nodes. The concepts of same source collision and different source collision were put forward. The collision caused by identical event triggering several nodes movement was regarded as same source collision and the collision caused by different events triggering different nodes movement was regarded as different source collision. All these two collisions can cause the decline of network data stream and the waste of nodes energy. Therefore, it is necessary to realize collision avoidance for MAC layer based on existing MAC protocol of wireless sensor. The collision aware MAC (CAMAC) using the idea of filter and power control was proposed. The principle of CAMAC handling different collisions was discussed, and CAMAC was simulated by NS.