Non-negative Matrix Tri-Factorization （NMTF） is an important part of the latent factor model. Because this algorithm decomposes the original data matrix into three mutually constrained latent factor matrices， it has been widely used in research fields such as recommender systems and transfer learning. However， there is no research work on the interpretability of non-negative matrix tri-factorization. From this view， by regarding the user comment text information as prior knowledge， Partially Explainable Non-negative Matrix Tri-Factorization （PE-NMTF） algorithm was designed based on prior knowledge. Firstly， sentiment analysis technology was used by to extract the emotional polarity preferences of user comment text information. Then， the objective function and updating formula in non-negative matrix tri-factorization algorithm were changed， embedding prior knowledge into the algorithm. Finally， a large number of experiments were carried out on the Yelp and Amazon datasets for the cold start task of the recommender system and the AwA and CUB datasets for the image zero-shot task to compare the proposed algorithm with the non-negative matrix factorization and the non-negative matrix three-factor decomposition algorithms. The experimental results show that the proposed algorithm performs well on RMSE （Root Mean Square Error）， NDCG （Normalized Discounted Cumulative Gain）， NMI （Normalized Mutual Information）， and ACC （ACCuracy）， and the feasibility and effectiveness of the non-negative matrix tri-factorization were verified by using prior knowledge.

To combat low efficiency of Artificial Bee Colony (ABC) algorithm on solving combinatorial problem, a parallel ABC optimization algorithm based on Spark was presented. Firstly, the bee colony was divided into subgroups among which broadcast was used to transmit data, and then was constructed as a resilient distributed dataset. Secondly, a series of transformation operators were used to achieve the parallelization of the solution search. Finally, gravitational mass calculation was used to replace the roulette probability selection and reduce the time complexity. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. The experimental results show that the proposed algorithm provides a 3.24x speedup over the standard ABC algorithm and its convergence speed is increased by about 10% compared with the unimproved parallel ABC algorithm. It has significant advantages in solving high dimensional problems.

Aiming at improving the users' high viscosity in social networks, an algorithm based on user loyalty in social network system was proposed. In the proposed algorithm, double Recency Frequency Monetary (RFM) model was used for mining the different loyalty kinds of users. Firstly, according to the double RFM model, the users' consumption value and behavior value were calculated dynamically and the loyalty in a certain time was got. Secondly, the typical loyal users and disloyal users were found out by using the founded standard curve and similarity calculation. Lastly, the potential loyal and disloyal users were found out by using modularity-based community discovery and independent cascade propagation model. On some microblog datasets of a social network, the quantitative representation of user loyalty was confirmed in Social Network Service (SNS), thus the users could be distinguished based on users' loyalty. The experimental results show that the proposed algorithm can be used to effectively dig out different loyalty kinds of users, and can be applied to personalized recommendation, marketing, etc. in the social network system.

Aiming at the problem that the existing evaluation methods do not take into account consumers' risk attitude, a subjective trust model based on consumers' risk attitude was proposed. Firstly, the historical information of entity evaluation attributes was converted into the interval number by using set-valued statistics theory. Then, by introducing risk attitude factor, the interval evaluation matrix was transformed into the evaluation matrix with risk attitude. Subsequently, the trust level of the entity was calculated by using the idea of relative closeness. Finally, the simulation results verify that the proposed method can make better trust decisions by considering risk attitude of consumers. The simulating experiment of anti-fraud further confirms the feasibility of the subjective trust model.

In Wireless Sensor Network (WSN) clustering routing algorithm, sensors energy consumption imbalance will result in "energy hole" phenomenon, and it will affect the network lifetime. For this problem, an energy-balanced unequal clustering routing protocol based on game theory named GBUC was put forward. In clustering stage, WSNs were divided into clusters of different sizes, the cluster radius was determined by the distance from cluster head to sink node and the residual energy. By adjusting the cluster head in the energy consumption of communication within the cluster and forwarding data to achieve energy balance. In inter-cluster communication phase, a game model was established by using the residual energy efficiency and link reliability as the benefit functions, using its Nash equilibrium solution to get joint energy balancing, optimal transmission path of link reliability, thereby improving network performance. The simulation results show that, compared with Energy-Efficient Uneven Clustering (EEUC) algorithm and Unequal Clustering Energy-Economical Routing (UCEER) algorithm, the GBUC algorithm has significantly improved the performance in balancing node energy consumption and prolonging the network lifetime.

Traditional theory-deduced route choice always has large deviation from the actual one in complex rail transit network. The signaling data were collected from the passengers' mobile phone in rail wireless communication network. According to these data, a new travel route identification algorithm was proposed based on normal location update. Meanwhile, concerning the data missing, a repair algorithm was also put forward by using other signaling data of users to deduce their actual travel route by the K shortest paths. And the route validity would be checked to get the actual travel route. Finally, typical application in Beijing rail transit network was selected to validate this algorithm. The application results show that the algorithm has a good performance in illustrating the actual travelers' travel behaviors.

The existing technology of color separation, especially the spot color separation, can no longer meet the requirements of prepress processing efficiency or printing quality.Aimed at this situation, the fuzzy C-means clustering algorithm(FCM) was put forward. The algorithm, based on the classification of pixels, carried fuzzy clustering on the grayscale of images in order to get image clustering center at first, and then put each pixel to the corresponding category according to the grayscale of each pixel and the maximum membership degree. Experimental result shows that image segmentation based on fuzzy rules is intuitive and easy to realize and has achieved a good segmentation effect.

A new data sharing scheme was proposed to solve the problem of error-tolerant search and fine-grained access control. This new scheme adopted the technology of locality-sensitive hashing and the predicate encryption, which allowed users to search for keywords in an error-tolerant manner, and modified the users access rights easily by updating the encrypted data. The computational complexity of updating is more optimized than the existing scheme. The theoretical analysis shows that the proposed solution is correct, safe and effective.

To make the statistical measures derived from Gray Level Co-occurrence Matrix (GLCM) provide better information about the texture, sufficient experiments were done on the Brodatz pictures. Firstly, by testing the effect of each parameter on the key statistics，the changing rule of the statistics along with different parameters was obtained and proper parameters were advised. Then the stability of these statistics was analyzed when the image was rotated or its size was changed. The experimental and analytical results provide valuable reference for creating GLCM better and achieving image retrieval based on texture information.