To meet the needs of data sharing in the context of digitalization currently， and take into account the necessity of protecting private data security at the same time， a blockchain smart contract private data authorization method based on TrustZone was proposed. The blockchain system is able to realize data sharing in different application scenarios and meet regulatory requirements， and a secure isolation environment was provided by TrustZone Trusted Execution Environment （TEE） technology for private computing. In the integrated system， the uploading of private data was completed by the regulatory agency， the plaintext information of the private data was obtained by other business nodes only after obtaining the authorization of the user. In this way， the privacy and security of the user were able to be protected. Aiming at the problem of limited memory space in the TrustZone architecture during technology fusion， a privacy set intersection algorithm for small memory conditions was proposed. In the proposed algorithm， the intersection operation for large-scale datasets was completed on the basis of the ??grouping computing idea. The proposed algorithm was tested with datasets of different orders of magnitude. The results show that the time and space consumption of the proposed algorithm fluctuates in a very small range and is relatively stable. The variances are 1.0 s² and 0.01 MB² respectively. When the order of magnitudes of the dataset is increased， the time consumption is predictable. Furthermore， using a pre-sorted dataset can greatly improve the algorithm performance.

Collaborative filtering was applied to automatically recommend categories for a Chinese Wikipedia article. Four typical semantic features namely incoming link, outgoing link, incoming link categories and outgoing link categories, were adopted to represent articles. Among all the categories of articles similar to target article, several most similar categories were chosen as the recommendation results to the target article, via calculating the similarity value between them. The experimental results show that the four semantic features have efficient performance in Wikipedia article representation. And the collaborative filtering method is also proved to be effective in recommending proper categories for Chinese Wikipedia articles.

In order to improve the accuracy and recognition speed of the remote sensing image classification, this paper put forward a new algorithm of remote sensing image classification based on K-type Support Vector Machine （SVM）,and this algorithm used texture features extracted by gray level co-occurrence matrix combined with the spectral ones for classification. The classification simulation tests were done with two groups of Landsat ETM+data. The results show that the new algorithm can improve the accuracy and efficiency of the classification, raise generalization ability, and K-type SVM is a superior classifier to the Radial Basis Function (RBF) SVM.

When Artificial Bee Colony (ABC) algorithm is applied to the load balancing system, local intensive resources may arise due to improper handling of the relationship between requests. Therefore, an improved adaptive ABC algorithm was proposed. By introducing other types of requests, the request accumulation of the similar type got blocked. Then, not only the availability but also the system throughput had been improved. The experimental results show that either the system throughput or the scalability of the load balancing mechanism based on ABC algorithm, is applicable to cloud computing environments. The improved adaptive ABC algorithm is superior to traditional algorithms more than 5% in system throughput.

It plays an important role in realizing cloud computing to implement the load balance of accessing resources. Therefore, based on the characteristics of cloud computing environment, an improved fuzzy clustering analysis algorithm was proposed in this paper. Furthermore, integrating Particle Swarm Optimization (PSO) algorithm with fuzzy C-means algorithm improved the algorithm accuracy. Then, using the improved fuzzy clustering algorithm in analyzing utilization ratio of I/O and Central Processing Unit (CPU) of all computing nodes, each node was divided into an affirmatory collection. And it represented its load level, as a basis for judging the node which needed to transfer tasks, so as to achieve the load balance. According to the experimental results, in terms of algorithm accuracy, no matter what is in the UCI machine learning repository, or for the proposed load balancing mechanism, the improved fuzzy clustering algorithm's reached the traditional one's by 110%. Besides, it surpasses the traditional one in the stability of the algorithm.

The multicast tree in MAODV is reconstructed frequently because of the nodes mobility, making the cost of routing and delay of transmission increase significantly. The neighbor change ratio based stable path selection method was proposed to overcome the shortcomings. And a new neighbor change ratio calculation method was put forward, which did not need to send Hello messages timely. Based on this new method, a stable path based MAODV (SP-MAODV) multicast routing protocol was given with a stable path selection and less hops. The simulation results on data packet transmission rate, routing overhead, average end-to-end delay and delay jitter show that the new protocol reduces interruption probability of the path and improves performance of the protocol.