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To analyze the effects of different seeding selection strategies on knowledge diffusion in education field, this study builds an ABMS spreading model and performs several simulation experiments. Besides, market segmentation is proposed as the methods of community recognition as, the effects of the mechanism of market segmentation on seeding user choosing strategies are in-vestigated, and knowledge diffusion efficiency is analyzed. Given the existing education community structure in social network of knowledge, and the forma-tion mechanism of network, and even if the multiple seeds locate in the same education community, they cannot effectively exert the knowledge diffusion function of each seeding node. Several studies have showed that random selec-tion strategy is more effective than the sensitive strategy without any market segmentation. The seeding strategy integrated with market segmentation is ca-pable of improving the efficiency of knowledge diffusion significantly. In the meantime, the sensitive seeding strategy under the education community recog-nition can achieve better knowledge diffusion efficiency.

Aiming at the problems available in the traditional method of cross-language text clustering, a Chinese-English cross-language text clustering algorithm based on Latent Semantic Analysis is put forward. [Method] With the method of Latent Semantic Analysis, Singular Value Decomposition of characteristic word-text matrix is carried out. The bilingual latent semantic space in Chinese-English is constructed to realize cross-language latent semantic association so as to reduce dimension and noise. The K-means algorithm which chooses the initial cluster center on the basis of the minimum similarity is adopted to avoid the effect of random selection of the initial cluster centers on the clustering effect.[Results] Experiment results show that the number of reserved characteristic words of each text s and the selection of the spatial dimension value k have certain impacts on the clustering result. When each text retains the top 15 characteristic words and k=200, the F-measure can be optimal. Compared to CLTC, 13.96 percentage points can be improved. [Conclusions] This method has greatly reduced the dimension of text space and improved the cross-language text clustering quality effectively. The clustering effect is better than CLTC.

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Analyze the time complexity of the algorithm, for multiple threads to execute multiple transactions at the same time, and map the result of the remainder of the prime number to the hash address. After calculation, the formula is a convergent array. The larger the value of x, the smaller the calculated value, indicating that the algorithm can complete the task in a certain limited time. Applying the probability of random prime numbers to the design of conflict management algorithms can avoid endless waiting for each other between transactions, and also avoid the waste of computing resources caused by cancelling earlier transactions. Compared with the radical algorithm of direct termination, this algorithm gives the transaction a certain waiting and retry time, avoids the long execution of the long transaction, and guarantees the commit time of the transaction.