Cloud computing is computing tasks distribution resources of a large number of computers in the subnet, to provide users with cheap and efficient computing power, storage capacity and service capabilities. Data mining is to find useful information in large data repository. Frequent flow of large amounts of data quickly and accurately find important basis for forecasting and decision, therefore, under the cloud computing environment parallelization frequent item data mining strategy to provide efficient solutions to store and analyze vast amounts of data has important theoretical significanceand application value.

In recent years, with the development of the Internet and network learning community rapid development, information technology is changing people's ways of e-learning with amazing speed. At the same time, learning trek, knowledge overload problems gradually emerged. Personalized learning recommendation has gradually become an important means for people to quickly learn and master the knowledge, but also reflects the people-oriented personalized learning mode. However, recommendation algorithm based on collaborative filtering still faces problems such as sparsity, scalability, cold start and precision. In this paper, a multi Agent collaborative filtering model based on double layer behavior is proposed for the problem of data sparsity and accuracy. Using social network relationship and dynamic agent perception and the ability to interact with learning, combined with individual behavior to measure the degree of interest, group behavior measure of trust and influence of learning resources for collaborative forecasting scale, reduce the score prediction error and the problem of learning trek.

Wet recycling method was utilized for recycling manganese from waste Zn-Mn batteries to obtain MnCO3. It was indicated that soaking time, the concentration and volume of acid all have great effects on the yield of MnCO3. The productivity of MnCO3 can reach 64.65%, when the concentration of HCl acid is 5mol/L, and with a volume of 40ml, under 180min soaking time. The productivity of MnCO3 can also reach 63.36%, when the concentration of HNO3 is 6mol/L, with 60ml volume by soaking for 80min. Furthermore, LiMn2O4 was synthesized in air atmosphere with the recycled MnCO3and Li2CO3 under different calcination temperatures. The electrochemical performances of prepared LiMn2O4 were studied and the results present preferable electrochemical properties.