Because there are many differences in real life scenes， human emotions are various in different scenes， which leads to an uneven distribution of labels in the emotion dataset. Furthermore， most traditional methods utilize model pre-training and feature engineering to enhance the expression ability of expression-related features， but do not consider the complementarity between different feature representations， which limits the generalization and robustness of the model. To address these issues， EE-GAN， an end-to-end deep learning framework including the network integration model Ens-Net was proposed. It took the characteristics of different depths and regions into consideration，the fusion of different semantic and different level features was implemented， and network integration was used to improve the learning ability of the model. Besides， facial images with specific expression labels were generated by generative adversarial network， which aimed to balance the distribution of expression labels in data augmentation. The qualitative and quantitative evaluations on CK+， FER2013 and JAFFE datasets demonstrate the effectiveness of proposed method. Compared with existing view learning methods， including Locality Preserving Projections （LPP）， EE-GAN achieves the facial expression accuracies of 82.1%， 84.8% and 91.5% on the three datasets respectively. Compared with traditional CNN models such as AlexNet， VGG， and ResNet， EE-GAN achieves the accuracy increased by at least 9 percentage points.

The unique advantages of Named Data Networking （NDN） make it a candidate for the next generation of new internet architecture. Through the analysis of the communication principle of NDN and the comparison of it with the traditional Transmission Control Protocol/Internet Protocol （TCP/IP） architecture， the advantages of the new architecture were described. And on this basis， the key elements of this network architecture design were summarized and analyzed. In addition， in order to help researchers better understand this new network architecture， the successful applications of NDN after years of development were summed up. Following the mainstream technology， the support of NDN for cutting-edge blockchain technology was focused on. Based on this support， the research and development of the applications of NDN and blockchain technology were discussed and prospected.

For the Job-shop Scheduling Problem (JSP) with the objective of minimizing the makespan, a hybrid algorithm combining with Imperialist Competitive Algorithm (ICA) and Tabu Search (TS) was proposed. Based on imperialist competitive algorithm, crossover operator and mutation operator of Genetic Algorithm (GA) were applied in the hybrid algorithm as assimilation to strengthen its global search ability. To overcome the weakness of imperialist competitive algorithm in local search, TS algorithm was used to improve the offspring of assimilation. The hybrid neighborhood structure and a novel selection strategy were used by TS to make the search more efficient. By combining with the ability of global search and local search, testing on the 13 classic benchmark scheduling problems and comparing with other four hybrid algorithms in recent years, the experimental results show that the proposed hybrid algorithm is effective and stable.