Simulation Analysis of Collaborative Efficiency Improvement of Innovative Talents Management in Colleges and Universities Based on BP Neural Network

Abstract

Objectives: In recent years, it is more and more difficult to manage innovative talents. In order to improve the collaborative efficiency of innovative talents management, this paper presents a simulation analysis of collaborative efficiency of innovative talents management in Colleges and Universities Based on BP neural network algorithm. Methods: Data simulation technology is used to establish talent management model. This model puts forward the optimization scheme from the algorithm flow, and improves the synergy of talent management by using data transformation technology. This model is analyzed from two aspects of universities and talents. BP neural network algorithm is added to the calculation of management efficiency to realize the sequence optimization of data. Results: In order to test the authenticity and efficiency of the algorithm in the talent management model, a comparative experiment is set up to analyze the results. The test results show that the accuracy of the optimized data analysis model is generally above 95%, while the accuracy of the traditional algorithm is generally below 80%, the collaborative efficiency calculation time of talent management model is the shortest, averaging only about 15 seconds; the traditional model calculation time is very unstable, from short 12 seconds to long 45 seconds, the calculation span is very large, and the accuracy rate is low. Conclusion: The research shows that BP neural network algorithm can improve the synergy of management and optimize the management mode of innovative talents, which is worthy of further promotion.