Analysis of Human Resource Allocation Scheme for Digital Media Big Data Based on Recurrent Neural Network Model

Abstract

At present, all kinds of computing models used in human society are based on statistics. Due to the large amount of data, conventional statistical methods cannot solve these problems well. In view of the concealment of data, the processing of large data plays a great role in the rational allocation of human resources, training professional talents, improving the operation of human resources, and improving the use and efficiency of human resources. This paper combines the method of human resource allocation based on recurrent neural network and conventional human resource allocation, in order to find a suitable method for personnel position selection and recommendation in the field of talent work. In terms of algorithm test, the F1 value of the proposed method is 0.823, which is 20.1% and 7.4% higher than the previous two methods, respectively, indicating that the method can effectively improve the hidden features, improve the training effect, and improve the performance of the model.