Optimization study of professional recognition based on big data

With the continuous development and deepening of the Internet, cloud computing, SG and other information technologies, big data has gradually penetrated into every aspect of all industries, and colleges and universities, as an information-intensive service industry, are the key areas for the application of big data technology. Under the background of big data, colleges and universities integrate traditional business with big data technology to provide differentiated and accurate statistics. In this paper, an optimization study on the survey of professional recognition is conducted by using big data technology. A multi-strategy improved hybrid whale optimization algorithm and ant colony optimization algorithm are proposed and used to optimize support vector regression and random forest model parameters. The construction of the optimization algorithm found that building a channel system of big data can improve the professional recognition of colleges and universities, and the use of big data technology improves the core management level. Finally, it is used in the actual data set to verify the effectiveness of the algorithm to optimize the model parameters.