With the continuous popularization of mobile Internet, today's society has entered the information age in an allround way. All kinds of data and information are popping up and flooding people's lives. To better manage this data and find the content you need in an information explosion environment, you need to apply cloud computing technology. Based on cloud computing and mobile Internet technology, this paper adopts the method of data mining, according to the characteristics of public opinion analysis system, and combines serial K-NN algorithm to design the parallel K-NN algorithm with the following ideas. Experimental data show that the content in the text library is filtered and contains the text of the crawling web page. The experimental results show that, in order to test the performance of k-NN algorithm, three test samples are constructed in this experiment, with the sample sizes of 5G, 10G and 15G respectively, and the information training sets of 2000 and 3000 respectively. In 5000, 6000; for 8000 and 10000 samples, the time cost of training is compared with the time cost of traditional serial mode. In view of the current mobile Internet information presents the characteristics of large data volume, complex data structure and diverse data content, in order to carry out information mining, the basic process of information mining should be clarified first, and then the corresponding mining technology should be used to realize effective information mining.
₹10000 (INR)
IEEE-2021