Big data refers to extremely large, sophisticated, and varied informational components. Better methods for controlling dynamic abilities, experiences, and interaction development are necessary. With the help of Massive Open Online Courses’ big data, this essay will examine how to research and analyse the flipped classroom in college English courses (MOOCs). Also covered is data mining. This paper addresses the data mining-based issue of big data in MOOCs. The idea of big data and associated algorithms is then further developed in this paper. In this essay, the flipped classroom for college English courses based on MOOCs is designed and examined. According to the experimental findings, 44.78 percent of students believe the MOOC learning platform is helpful after the implementation of the university English flipped classroom based on big data from MOOCs, and 26.96 percent of students find it extremely helpful. It is clear that the big data college English courses taught in a flipped classroom using MOOCs have had some educational impact.

One of the most effective approaches to improve resource usage efficiency and degree of resource collecting is to integrate resources. Many studies on the integration of information resources are also available. The search engines are the most well-known. At the same time, this article intends to optimize the integration of British and American literature information resources by employing distributed cloud computing, based on the needs of British and American literature. This research develops a model for the dispersed nature of cloud computing. It optimizes the method by fitting the mathematical model of transmission cost and latency. This article analyzes the weaknesses of the current British and American literature information resource integration and optimizes them for the integration of British and American literature resources. The Random algorithm has the longest delay, according to the results of this paper’s experiments (maximum user weighted distance). The algorithms NPA-PDP and BWF have longer delays than the algorithm Opt. The percentage decline varies between 0.17 percent and 1.11 percent for different algorithms. It demonstrates that the algorithm presented in this work can be used to integrate and maximize information resources from English and American literature.