Duing to the satellite network has high requirement for security and fault-dealing ability, with Software Defined Network (SDN) technology introduced, the central controller was set in the network to enhance the network's fault-dealing ability. Firstly, a satellite network model was designed based on the SDN idea, and the satellite's operating parameters on the three-layer orbit were calculated and the constellations were built. Then, the method of hierarchical routing was used to design a fault tolerant routing mechanism for satellite network. Finally, the simulation experiments were carried out on the Mininet platform, and the experimental results of Fault-Tolerant Routing algorithm (FTR) were compared with the results of inter-Satellite Routing algorithm based on Link Recognizing (LRSR) and Multi-Layered Satellite Routing algorithm (MLSR). The comparison results show that in the case without damaged nodes or links in the network, the total routing delay of FTR is reduced by 6.06% on average compared with that of LRSR, which shows the effectiveness of introducing SDN centralized control; the packet loss rate of FTR is reduced by 25.79% compared with that of MLSR which also targets the minimum delay, which shows the effectiveness of temporary storage routing mechanism design for the Medium Earth Orbit (MEO) satellites. When the failure of nodes and links in the network is serious, FTR has the total routing delay 3.99% lower than LRSR and 19.19% lower than MLSR, and has the packet loss rate 16.94% lower than LRSR and 37.95% lower than MLSR, which shows the effectiveness of fault tolerance of FTR. The experimental results prove that the fault tolerant routing mechanism of satellite network based on SDN has better fault tolerant capability.

To solve the limitation and accuracy of matrix decomposition in Collaborative Filtering (CF) algorithm, a Collaborative Filtering algorithm based on Bounded Matrix low rank Approximation (BMA) and Nearest neighbor model (BMAN-CF) was proposed to improve the accuracy of item scoring prediction. Firstly, the matrix factorization algorithm of BMA was introduced to extract the implicit feature information of sub-matrix and improve the accuracy of neighborhood set search. Then, the target users' scores on target items were respectively predicted according to the traditional user-based and item-based collaborative filtering algorithms. And the equilibrium factor and control factor were used to dynamically balance the two prediction results, the target users' scores of items were obtained. Finally, the data was partitioned, and the proposed algorithm was parallelized in Hadoop environment by using the characteristics of MapReduce computing framework. The experimental results show that, the BMAN-CF has higher rating prediction accuracy than other matrix factorization algorithms, and the speedup experiment shows that the proposed parallelized algorithm has better scalability.