Research and Practice on the Talent Evaluation Model of the First-class Undergraduate Major in Network Engineering for Emerging engineering

In view of the continuous quality improvement of network engineering education in the context of emerging engineering, we propose a talent evaluation method in the field of network engineering based on deep reinforcement learning with time windows. Firstly, a mathematical model of talent evaluation is established, and then a comprehensive index system is built from four aspects: engineering awareness, engineering foundation, engineering ability and engineering innovation. Secondly, the time window mode is used to divide the time slots of the data source, and the evaluation results are solved through the dynamic interaction between the agent and the environment. Further, the evaluation results are used as feedback factors to continuously improve the quality of education. Finally, we take "Nanjing University of Posts and Telecommunications-Network Engineering" as an example to conduct experimental simulation. The results show that the proposed method can obtain more accurate and reliable evaluation conclusions.