Algorithm Analysis for Big Data in Education Based on Depth Learning

The construction of campus network has provided an advanced comprehensive information environment for teaching, scientific research and management of colleges. In the process of digitization and intelligentization, the data produced by all kinds of application systems in college are growing, and the large data environment of campus has been formed. Big data of college contain abundant information, so we need to use new data storage and analysis tools to store and analyze huge amounts of college data and get useful information from them. In this paper, a depth learning analysis algorithm based on Map Reduce is proposed to deal with college data. Using Map Reduce parallel computing framework to achieve campus data computing, we studied the analysis and application systems of campus big data in different themes and levels and dug out valuable information hidden behind college data. The experimental results show that the high school data mining algorithm based on Map Reduce is effective. It provides new research ideas for large data mining in colleges and provides technical reference for the construction of smart campus.