Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning

Big data values data processing to ensure effective value-added data. With the rapid development of the cloud era, the coverage of big data has gradually expanded, and it has received wide attention from all walks of life. In the process of modern social development, big data analysis is gradually applied to the future development planning, risk evaluation and integration of market development status. With the rapid development of many fields of society, the flow of information has gradually expanded, and the Internet has developed more rapidly, prompting the application of big data in various fields. Machine learning is a multidisciplinary study of how computers use data or past experience. With the ability to independently improve specific algorithms, the computer acquires knowledge through learning and achieves the goal of artificial intelligence. Big data and machine learning are the major technological changes in the modern computer world, and these technologies have had a huge impact on all walks of life. At present, with the rapid development of the Internet, mobile communications, social networks and the Internet of Things, these networks generate large amounts of data every day, and data become the most important information resource of today. Some studies have shown that in many cases, the larger the amount of data, the better the data will be for machine learning. On this basis, this paper proposes an online client algorithm based on machine learning algorithm for IoT unstructured big data analysis and uses it in other big data analysis scenarios. Use the online data entered by the customer to implement background data mining, the parallel way to verify its efficiency through machine learning algorithms such as K-nearest neighbor algorithm.