Evaluation of the Impact of the Introduction of Overseas High-Level Talents on the Development of Central Cities with Artificial Neural Network

Abstract

For the central cities, the transformation and upgrading of the industrial structure, the continuous deepening of innovation capabilities, and the improvement of sustainable development capabilities, human resources are playing an increasingly important role. In the modern era, assembling an army of highly educated, high-quality, and highly talented personnel has become a practical demand of talent development strategy. In order to respond to national policies, keep up with the trend of the times, and give play to the comparative advantages of talent competition, the primary task of central cities is to do a good job in the introduction of overseas high-level talents. It is necessary for the central cities to establish a mechanism for the introduction of overseas high-level talents with the government as an important task and to formulate targeted talent introduction policies according to the local characteristics of the central cities. It is very important to evaluate the impact of the introduction of overseas high-level talents on the development of central cities so that the central cities can formulate talent introduction strategies according to the actual situation. This work uses an artificial neural network to evaluate the impact of the introduction of overseas high-level talents on the development of central cities. Aiming at the problem that the evaluation accuracy and computing efficiency of the deep learning-based method decrease due to the proliferation of neural network layers, and the improved residual network model is proposed. On the one hand, a multiscale feature fusion block is designed in the first layer of the network model, which can extract the multiscale feature information in the original special. On the other hand, the residual block is optimized and improved by using depthwise separable convolution to remove the computational burden on the network.