Construction and implementation of a college talent cultivation system under deep learning and data mining algorithms

This paper aims to improve the quality and efficiency of talent cultivation in colleges and universities and effectively analyze and count the number of talents. The cultivation of computer professionals in higher vocational education is taken as the research object. First, a talent cultivation information system for colleges and universities is developed, using deep learning neural networks and data mining algorithms and introducing recurrent neural networks (RNN) and a fusion attention model. With a view to the needs of employment forms and employment data, a diversified talent demand cultivation program is formulated. Based on this, an efficient talent cultivation quality indicator system is established through analyzing talent cultivation data in higher vocational education. Finally, the effectiveness of the system is verified by the performance analysis. The results show an average prediction accuracy for the proposed Attention RNN Word2 algorithm of 80.61%, which is significantly higher than achieved by the latest research algorithm model, and the talent training has a classification accuracy of 82%. This model is not only feasible for categorizing computer professional personnel cultivation but can also accurately reflect problems arising in personnel cultivation, and the results are made obvious by visualization. To sum up, the system reported here can give an intuitive demonstration of the quality of computer professional personnel cultivation in higher vocational education. This research provides a theoretical basis for related research into the cultivation of computer professionals in higher vocational education for application in industry, universities, and research.